Cryptocurrency is no longer that of a topic solely for traders and tech enthusiasts, as crypto ETFs have worked their way into mainstream retirement accounts, bringing with them many questions to financial planners. The question this study addresses is whether small Bitcoin and Ethereum ETF allocations actually improve the risk-adjusted performance of a traditional balanced retirement portfolio. To find out, five different portfolio constructions were tested using real ETF return data, with performance measured across Sharpe ratio, Sortino ratio, maximum drawdown, and correlation, all with quarterly rebalancing built in. Every portfolio that included cryptocurrency outperformed the standard baseline on risk-adjusted return metrics, though drawdown did increase as the allocation grew. What this tells us is that small, structured cryptocurrency allocations have the potential to improve retirement portfolio performance for the right investor, but suitability still needs to be worked out on an individual basis, something financial planners can take directly into their practice.
Bitcoin’s Proof-of-Work mechanism is energy intensive, exceeding the electricity consumption of a medium-sized country. As the adoption accelerates, it become a concern. Most studies analyzed its energy consumption, emissions, and price in isolation. This study examines the relationship between the energy consumption and energy mix of Bitcoin and its market performance, moderated by quality of regulation, using a time-series of secondary data from reputable resources e.g. Cambridge Bitcoin Electricity Consumption Index,, the Worldwide Governance Indicators, etc. Regression analyses are employed to test the hypotheses. Eight of nine null hypotheses failed to reject. However, energy consumption was found to have a significant positive relationship with market return. It is, however, likely that this finding captures shared underlying drivers of Bitcoin’s price and its energy consumption, as well as possible reverse causality. Energy mix was found to have no significant effect on the three alternative outcomes, aligned with the fungibility of Bitcoin. Furthermore, regulatory was found not to significantly moderate also likely due to the narrow variation in the Indonesia’s scores during the study period. The study identified that markets do not reward sustainable mining with a market premium, implying that the transition towards renewable-powered mining in Indonesia requires more policy intervention.
Predicting extreme price movements in high-frequency financial markets is a challenging task due to non-stationarity, heavy-tailed return distributions, and severe class imbalance. In particular, rare but impactful events are often difficult to detect using conventional modeling approaches, which typically treat extreme movements as isolated observations. This study proposes a volatility-aware approach for extreme event detection using high-frequency Bitcoin limit order book (LOB) data. Motivated by empirical evidence of volatility clustering, the target formulation is extended to incorporate both large future returns and high-volatility regimes. This redefinition increases the proportion of informative samples and aligns the learning objective with the underlying market dynamics. Using a tree-based model (XGBoost) with time-series cross-validation and imbalance-aware evaluation, the proposed method achieves a Precision-Recall AUC of approximately 0.40, significantly outperforming the baseline formulation with a PR-AUC of around 0.06. This represents more than a sixfold improvement in detecting rare events. The results highlight that target design plays a critical role in financial machine learning, often exceeding the impact of model complexity. By incorporating volatility structure into the labeling process, the proposed approach provides a more effective and realistic framework for extreme event detection in high-frequency cryptocurrency markets.
Abstract Distributed ledgers – decentralized databases maintained by network consensus – are often modeled as directed acyclic graphs (DAGs) to capture the causal structure of data addition. Although blockchain systems like Bitcoin use linear chains, alternatives such as tangle in IOTA employ random DAGs. In such mechanisms each new transaction approves multiple predecessors selected through a randomized process. Prior work has established a fluid-limit approximation of the tangle’s growth, governed by a delay differential equation. In this paper we go beyond the fluid limit by analyzing the next-order behavior. We show that the fluctuations around the deterministic limit converge to a Gaussian process and derive a stochastic delay differential equation (SDDE) that describes this next-order approximation.
Ádám Bereczk, Zoltán Musinszki, Erika Szilágyiné Fülöp, Bettina Hódiné Hernádi
This study investigates the allocation of pre-sale capital by blockchain technology-based startup ventures, with a specific focus on the Play-to-Earn (P2E) segment within the Web3 ecosystem, and its impact on token price performance. Our aim is to determine the proportion of initial capital that P2E startups, according to their business plan (whitepaper), allocated to key areas such as team and advisor expenses, marketing activities, and product development. Subsequently, this research centers on the question of how the focal areas of pre-sale capital utilization (team, marketing, development) correlate with the subsequent price performance of the tokens issued by these startups. The timeliness and relevance of this topic are underscored by the dynamic evolution of blockchain technology and the P2E model, as well as the critical role of startups' capital allocation decisions. Understanding how the utilization of initial funding influences long-term value is also of paramount importance for investors. Based on the results, while excessive marketing expenditures may offer a project short-term benefits, this strategy can potentially have negative long-term consequences. A project's financial viability is contingent upon competent human resources and the insights of external experts; nevertheless, these elements alone are not definitively sufficient. The significance of product development was only evident when the effect was measured in Bitcoin terms; no correlation was found when measured in Dollars.
This paper presents our system for Task 3 of the CLEF 2026 FinMMEval Lab, which requires daily long, flat, or short trading decisions for Bitcoin (BTC) and Tesla (TSLA) using news and historical market data. We formulate the problem as a discrete-action Markov Decision Process and compare four deep reinforcement learning algorithms: Policy Gradient (PG), Proximal Policy Optimization (PPO), Deep Q-Learning (DQL), and Deep Deterministic Policy Gradient (DDPG). The agents use technical indicators, cyclical calendar encodings, and daily news sentiment scores produced by LLaMA 3.2 1B. To reduce overfitting and align training with the objective of outperforming buy-and-hold, we introduce an alpha reward based on excess market return and randomize episode start dates. Hyperparameters are optimized with Ray Tune over 180 trials per algorithm-asset pair, with early stopping and model selection based on validation Sharpe ratio. On the CLEF Task 3 test set, DDPG achieves the strongest overall performance. DQL was selected a priori for the live endpoint because it obtained the highest validation Sharpe ratio, with selection performed without access to the test period. For TSLA, DDPG and DQL achieve cumulative returns of 54.96% and 52.62%, respectively, compared with 16.45% for buy-and-hold. For BTC, DDPG achieves a positive return of 1.58% while buy-and-hold declines by -34.27%. The results also reveal a substantial validation-to-test generalization gap, highlighting the difficulty of transferring policies selected in bull-market conditions to a bear-market regime.
ABSTRACT The rise of cryptocurrency has transformed the way individuals perceive and participate in investment activities. As digital assets continue to gain global recognition, major digital assets, including Bitcoin, Ethereum, Ripple (XRP), and Tether, have gained significant interest among investors seeking alternative avenues for wealth creation. The growing adoption of blockchain technology, expansion of digital financial services, and increasing accessibility of cryptocurrency trading platforms have contributed to the rising popularity of crypto investments in India. The present study explores the emerging cryptocurrency investment trends in India through the analysis of secondary information collected from scholarly articles, industry reports, government documents, and other credible sources. The research focuses on important areas including cryptocurrency adoption, market growth, investor demographics, regulatory developments, and investment behaviour. The findings indicate that investments in cryptocurrencies have experienced notable growth in India, particularly among younger investors, despite challenges related to market volatility and regulatory uncertainty. The study concludes that digital assets are gradually becoming an important part of the Indian investment environment and may continue to influence future investment patterns. Keywords:, Digital Assets, Investment Trends, Blockchain Technology, Investor Behaviour.
This paper seeks to assess the feasibility of utilizing Bitcoin as a currency within Türkiye. To achieve this, the research analyzes long-term cointegration relationships between Bitcoin and both the US Dollar and Euro, employing monthly data from November 2017 to February 2025 and utilizing the Fourier Shin cointegration test. The results of the cointegration tests, bolstered by Fourier series analysis, reveal significant long-term cointegration relationships between Bitcoin and both the USD and Euro. The DOLS analysis indicates that a 1% rise in Bitcoin leads to a 14% decrease in the USD price and a 17% increase in the Euro. These results imply that Bitcoin exhibits a high sensitivity to ex-change rates, positioning it as a speculative investment in the short term. The pronounced inverse correlation between the US Dollar and Bitcoin raises the possibility of Bitcoin serving as a substitute for the US Dollar.
This chapter explores the role of blockchain and cryptocurrency forensics in investigating Darknet-enabled cybercrime. Cryptocurrencies such as Bitcoin and privacy-focused coins are widely used in Darknet marketplaces because they support pseudonymous transactions that complicate tracing and attribution. The chapter examines forensic techniques for blockchain analysis, including address clustering, transaction graph analysis, and heuristic-based tracing. It also explains how illicit financial flows are concealed through mixers, tumblers, and chain-hopping strategies. In addition, the chapter reviews analytics tools used by law enforcement and cybersecurity professionals to detect suspicious patterns and link wallets to entities. Challenges related to privacy-enhancing cryptocurrencies, blockchain scalability, and legal considerations are discussed. Finally, emerging threats involving decentralized finance (DeFi) and cross-chain transactions are explored to provide researchers, forensic analysts, and policymakers with insights into illicit financial activity in the Darknet ecosystem.
Abstract This paper analyzes the electricity consumption of Bitcoin mining as a component of blockchain-based financial infrastructure and develops a hybrid forecasting framework that combines a Neural Network Autoregressive model with Exogenous Inputs (NARX) and Mixed Data Sampling (MIDAS). The specification embeds nonlinear state dependence within a feedforward neural network structured as a NARX and exploits mixed-frequency information from daily and monthly indicators to forecast weekly electricity consumption. A key methodological contribution lies in reframing exogenous variable selection as a ranking-based optimization problem grounded in individual explanatory power. To support this, a large language model (LLM)-assisted screening procedure is used to construct a theory-consistent pool of candidate predictors drawn from the finance, energy and cryptocurrency literature. From this pool, an optimization-based strategy identifies a parsimonious subset of variables that minimizes forecast error within the NARX–MIDAS framework. Empirical results demonstrate that the optimized model significantly outperforms benchmark specifications, achieving reductions of 15–20% in root mean squared error and 10–12% in mean absolute error. Beyond predictive performance, the proposed framework yields interpretable insights into how macroeconomic conditions, policy-related uncertainty and financial market dynamics influence Bitcoin mining activity. These findings have direct implications for risk management, energy planning and regulatory oversight in blockchain-based financial systems, highlighting the value of integrating LLM-assisted knowledge extraction with rigorous optimization-driven forecasting methodologies.
Arthur G. Bubolz, Abreu Quevedo, Giancarlo Lucca, Rafael A. Berri · 6 authors
The growing use of Bitcoin as a decentralized digital asset and investment tool has sparked strong interest in understanding its market behavior. This study presents a new approach to analyze Bitcoin market sentiment by combining on-chain and financial data with social media posts. Unlike models that aim to predict prices, this work focuses on explaining market sentiment using blockchain transactions, historical price data of Bitcoin, and daily Twitter sentiment classifications. The method merges sentiment trends with on-chain and financial metrics, normalized into a dataset for detailed market analysis. Multiple machine learning models were tested using cross-validation, with Gradient Boosting (XGBoost) emerging as the most reliable model for classifying sentiment, achieving an average F1-score of about 0.84. SHAP (SHapley Additive exPlanations), a game theory-based method for model interpretability, was used to quantify the contribution of on-chain features to the model's predictions, improving transparency. The results indicate that this data combination yields meaningful predictive signals and insights, supporting data-driven cryptocurrency analysis and future improvements with deep learning.
O presente artigo analisa o custo-benefício energético de três mecanismos de consenso centrais no ecossistema de criptoativos – Proof-of-Work (PoW), Proof-of-Stake (PoS) e o modelo híbrido baseado em Proof-of-History (PoH) combinado com PoS – examinando como as diferenças de consumo energético entre esses paradigmas se relacionam a propriedades de segurança, desempenho e sustentabilidade econômica. A partir de dados recentes sobre o consumo energético de redes públicas de referência – entre as quais o Bitcoin, o Ethereum antes e depois da transição para PoS (Merge) e a Solana – discute-se em que medida a evolução dos desenhos de consenso permite reduzir o uso de eletricidade por ordens de grandeza, sem necessariamente comprometer segurança e descentralização. A metodologia combina revisão bibliográfica de estudos acadêmicos e relatórios técnicos sobre consumo energético em blockchains, análise de estimativas consolidadas de uso anual de eletricidade e de energia por transação e discussão conceitual dos trade-offs entre eficiência energética, robustez criptográfica, requisitos de hardware e impactos regulatórios. As evidências empíricas revisadas indicam que o Bitcoin, ancorado em PoW, mantém consumo anual estimado em torno de 120 a 130 terawatt-hora (TWh), ao passo que o Ethereum, após a migração para PoS em setembro de 2022, reduziu seu consumo em mais de 99,9%, operando com menos de 0,01 TWh por ano. Relatórios de eficiência energética apontam que redes que combinam PoH e PoS, a exemplo da Solana, apresentam consumo de energia por transação da ordem de centenas de joules, valor inferior tanto ao de redes PoW quanto ao de diversas redes PoS de menor vazão, embora existam ressalvas metodológicas e debates acerca dos efeitos de centralização de infraestrutura associados a requisitos elevados de hardware e conectividade. Conclui-se que PoS e esquemas híbridos com PoH oferecem vantagens substanciais em termos de eficiência energética, mas que a avaliação de custo-benefício deve incorporar conjuntamente a segurança econômica, a distribuição de poder entre participantes, a maturidade do ecossistema e o alinhamento com agendas de sustentabilidade que tendem a moldar a evolução da infraestrutura Web3 nas próximas décadas.
This chapter examines the dual nature of virtual currencies. It mainly focuses on Bitcoin’s role in both financial innovation and illicit finance. This chapter analyzes the core mechanisms of anonymity and decentralization that make cryptocurrencies attractive to criminal activity. It was exemplified in the landmark Silk Road darknet marketplace case. The discussion traces the evolving regulatory response, from initial enforcement actions to the development of structured frameworks such as the GENIUS Act for stablecoins and the CLARITY Act for digital asset market classification. Further analysis covers the application of traditional securities and commodities laws to decentralized finance (DeFi). The MNGO Markets illustrated its exploitation case. The discussion centers around two blockchain applications: cross-border payments and the creation of immutable smart contracts to comply with General Data Protection Regulation (GDPR). This chapter concludes that cryptocurrencies exist as a dual-purpose technology. The system requires a sophisticated regulatory approach that lowers both financial crime risks and market integrity threats while preserving the potential for technological innovation.
Mohotarema Rashid, Lingzi Hong, Junhua Ding, K. S. M. Tozammel Hossain
Large language model (LLM) trading agents show promising performance in equity markets, yet remain narrowly focused on US equities with little evidence from live deployment. We present Fin-Analyst, a hybrid agent for FinMMEval 2026 Task 3: an eight-specialist LLM pipeline over news, SEC filings, fundamentals, analyst forecasts, technical indicators, and social sentiment, aggregated by a Meta-Agent for Tesla (TSLA), and a lightweight rule based three-signal vote for Bitcoin (BTC). On the final official leaderboard (accessed 2026-07-05), Fin-Analyst ranks first of all agents on TSLA with a +13.51% return, +28.33 points over Buy-and-Hold (Sharpe 4.10, 88% win rate), while the BTC vote ends flat yet well above a sharply falling baseline. Relative to the interim performance, the asset ranking reversed, indicating that short live windows yield volatility-sensitive rankings. Ablation identifies event-driven 8-K disclosures as the most influential TSLA signal. Error analysis shows that the memoryless agents repeat wrong calls for days at a time, and that the fixed-threshold BTC rules lost money by trading on noise in a sideways market while the LLM pipeline gained under similar conditions, motivating a memory-aware, LLM-based successor for both assets.
Yongqiang Du, Chen-Xun Weng, Feng Xie, Ming-Yang Li · 13 authors
Popularized by the Bitcoin cryptocurrency, blockchain technology establishes a decentralized digital framework that utilizes cryptographic and consensus protocols to secure data against unauthorized modification. Consequently, blockchain has found broad adoption across diverse fields, including finance, data management, healthcare, and digital asset governance. In the quantum computing era, a paramount objective for blockchain is to preserve its foundational advantages of cryptographic integrity and decentralized fault-tolerant resilience. In principle, quantum digital signatures and quantum Byzantine agreement protocols offer foundational security guarantees and tolerate up to one-half of malicious nodes for blockchain. However, the practical realization of such a quantum-enhanced blockchain remains a significant and multifaceted challenge. Here, we propose and experimentally demonstrate a fully operational hybrid quantum blockchain architecture built on photonic integrated circuits and deployed over commercially available classical telecommunications infrastructure. The system achieves a fault tolerance of nearly one-half, surpassing the classical limit, while reaching consensus on a timescale of seconds. A deployed food traceability application validates the practicality of the proposed architecture, achieving a throughput of approximately 500 transactions per second. This work establishes a foundation for practical quantum blockchains, enabling secure, scalable, and decentralized information processing in the emerging quantum era.
This study investigates the growing role of stablecoins within the global financial system and examines their potential integration into traditional foreign exchange markets. Despite the rapid expansion of stablecoins, empirical evidence comparing their market dynamics with those of non-stable cryptocurrencies remains limited. To address this gap, the study adopts a descriptive case study design based on documentary analysis and secondary quantitative market data. The documentary review establishes the theoretical foundations of stablecoins and their relevance to foreign exchange markets, while the quantitative analysis relies on market data collected from CCData, DefiLlama, and Statista. Weekly market observations covering the period from April 2019 to May 2024 were analyzed using descriptive statistics, comparative analysis, volatility measures, Pearson correlation analysis, and one-way ANOVA. The findings reveal that stablecoins exhibit significantly lower price volatility than Bitcoin while maintaining high levels of market liquidity and trading activity. Among the analyzed assets, Tether (USDT) remains the dominant stablecoin, followed by USD Coin (USDC) and Binance USD (BUSD). The statistical analysis confirms significant differences between stablecoins and Bitcoin, highlighting the distinct market behavior of reserve-backed digital assets. These findings suggest that stablecoins have evolved beyond their traditional role as cryptocurrency trading instruments and are increasingly functioning as efficient mechanisms for cross-border payments, liquidity management, and decentralized finance applications. This study contributes to the literature by providing an integrated empirical comparison of stablecoins and non-stable cryptocurrencies while demonstrating how the stability, liquidity, and operational characteristics of reserve-backed digital assets may facilitate their future integration into traditional foreign exchange markets. The findings also provide practical implications for policymakers, financial institutions, and regulators seeking to develop secure and efficient digital payment infrastructures supported by appropriate regulatory frameworks.
Harrison Rush, Vincent Davis, Simone Antonelli, Vikash Singh · 6 authors
We address liquidity placement in the Bitcoin Lightning Network (LN): given a fixed budget, which channels should a node open to maximize its routing capacity? We cast this as a budget-constrained combinatorial optimization problem on graphs, selecting $k$ edge additions that maximize $s$--$t$ max-flow, a theory-grounded measure of routing capacity, and solve it with graph reinforcement learning. Our lightweight agent combines a message-passing policy network with proximal policy optimization (PPO) and action masking, and is trained under a hub-exclusion curriculum: the network's top hubs are removed from training subgraphs, forcing the policy to learn capacity-aware placement rather than hub attachment. In extensive experiments on real Lightning Network snapshots, our method consistently outperforms strong heuristic baselines on the max-flow objective across multiple seeds and unseen graphs. The agent has been deployed in production for peer recommendations, executing 4640 channel-open decisions that cumulatively allocate 267.3 BTC over $16 million across 30 managed nodes.
Pietro Saggese, Michael Sigmund, Burkhard Raunig, Esther Segalla · 6 authors
Cryptoassets are increasingly entangled with the traditional financial system, and how this activity integrates into national economies and behaves under stress bears on financial stability and the design of public digital money. However, blockchain pseudonymity and the lack of geographic identifiers force existing work to rely on indirect proxies to infer and locate market participants. Here we use a regulatory registry that directly identifies the on-chain addresses of all crypto-asset service providers (CASPs) registered in Austria, reconstructing their on-chain transaction activity across Bitcoin, Ether, USDC, and USDT through May 2025, and separating retail-like from institutionally mediated flows. We find that Austrian CASPs intermediate roughly USD 30 billion with external counterparties and are integrated globally rather than domestically. In value, this activity is dominated by a few institutional counterparties; in number, by retail-like ones. Around three major shocks, the Terra-Luna collapse, the FTX bankruptcy, and the Silicon Valley Bank failure, the two groups respond through different mechanisms, and stablecoins do not act as a uniform safe haven. The clearest case is SVB, where retail-like deposits and institutional withdrawals are consistent with USDC's two-tiered redemption mechanism. These patterns are invisible in aggregate data. Registry-based, transaction-level measurement thus offers a reproducible, cross-jurisdictional basis for monitoring how cryptoasset markets transmit risk.
Leopold Müller, Jana Elsner, Thomas Niedermayer, Bernhard Haslhofer · 7 authors
Address clustering is an important technique in blockchain forensics, widely employed by law enforcement to trace illicit crypto asset flows. The multi-input heuristic (MIH), which clusters addresses potentially associated with the same entity, is the most widely used. Yet, despite its broad adoption, the MIH has rarely been evaluated against reliable ground truth data. We implement a reusable evaluation framework covering nine established metrics and apply it to ground truth address-to-entity mappings obtained directly from European crypto asset service providers under legally mandated reporting obligations. When evaluation is restricted to reported addresses, the MIH appears strong at dataset level: we observe no mergers between reported services and recover same-service address pairs with recall 0.71. However, this result is driven by one large service and ignores unlabeled addresses absorbed into full clusters. Metrics that assess the full clusters show substantially lower precision and recall (0.36 and 0.44), meaning that services are often only partially recovered or embedded in larger clusters. Entity-level results further reveal near-complete failures for some services. When MIH-based clusters are used to support criminal suspicion, preliminary seizure of crypto assets to secure later forfeiture/ confiscation, or as evidence in trial proceedings, prosecutors and judges must account for the heuristic's metric-dependent and entity-dependent reliability.
In the era of digital revolution many contemporary events that changed the world were shaped through the internet. Nowadays, the emergence of internet of things (IoT), combining physical objects with virtual networks is expected to have even more influence. This new 'decentralised' structure in the world raises questions such as power, governance and the notion of democracy online. The aim of this paper is to investigate these notions. We have taken the examples of Bitcoin and Wikipedia and examined their decision-making process. Our analysis has found some inconsistencies in their policies, that are in contradiction with democracy and consensus principles of governance. Starting from our findings, we present further improvements that can be used to achieve more democracy and equity in the digital context.
This paper introduces Crossroads, a smart contract layer for chain-abstracted assets. In Crossroads, assets from nearly any chain are represented on a single backend blockchain as ERC-20 tokens. As a result, any asset can participate in smart-contract-based exchange, lending, or privacy applications on a single unified platform. So while Crossroads offers cross-chain bridging, a common, partial approach to alleviating the fragmentation of the blockchain ecosystem today, this is just one service within Crossroads' general-purpose chain-abstraction model. Crossroads relies on key encumbrance: a threshold signing committee holds encumbered keys controlling assets on each integrated chain, signing transactions only as authorized by smart contracts on the backend blockchain. Asset movements are fee-efficient, as ownership changes are recorded on the backend blockchain and users may set the transaction fee for withdrawals. Crossroads enables permissionless, modular integration of new blockchains using pluggable oracles with flexible design options (zkBridge, TEE-based, hybrid). Asset deposits into Crossroads benefit from strong, chain-specific finalization guarantees, minimizing the risk of reorg attacks. Unlike existing bridges, however, third-party smart contracts in Crossroads can provide fast, optimistic access to funds before finalization completes. We prove that Crossroads satisfies soundness: given an honest quorum of signing committee members, any user can unilaterally generate a withdrawal transaction transferring their net balance to an account on an integrated blockchain. We implement a proof of concept across multiple public blockchains: Bitcoin, Ethereum, and Solana. We catalog a range of applications enabled by Crossroads, including universal wallets, cross-chain staking and lending, privacy-preserving payments, and private management of public blockchain assets.
We show that frictions at cryptocurrency exchanges, captured through consumer complaints, are reflected in the relative pricing of Bitcoin across U.S. trading venues. Using daily Bitcoin prices and CFPB complaint data, we find that average cross-exchange price deviations relative to Coinbase are significantly higher on days with at least one exchange-targeted Bitcoin complaint, indicating that Bitcoin on Coinbase trades at a larger relative discount on those days. The effect strengthens with complaint intensity. The results survive placebo tests using non-exchange complaints and diagnostics for omitted-variable bias. Complaints targeting Coinbase are associated with cumulative abnormal returns for Coinbase stock that are about 3.17 percentage points lower around complaint days. Overall, our evidence indicates that consumer complaints are associated with both cross-exchange Bitcoin price deviations and the equity valuation of the affected exchange.