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.
With the rapid advancement of decentralized finance (DeFi), security incidents related to cryptocurrency have become increasingly prevalent. After such incidents, attackers typically attempt to rapidly move stolen assets, concealing the origin of illicit funds and ultimately converting them into fiat currency. However, existing anti-money laundering (AML) methods struggle to cope with the semantic complexity of DeFi transactions. They either rely heavily on low-level token transfers, or perform protocol-agnostic money flow analysis, failing to capture the high-level intent of transactions. In this paper, we propose AMLGuard, a semantic-aware AML framework for account-based blockchains. AMLGuard tracks illicit fund flows from known malicious addresses by performing semantic analysis on complex DeFi transactions, enabling accurate and continuous laundering tracking. Given a complex transaction, AMLGuard combines static rule-based analysis with retrieval-augmented large language model (LLM) reasoning to infer implicit DeFi semantics, transforming raw transaction data into high-level semantic representations. Furthermore, for cross-chain transactions where laundering intent is not explicitly exposed, AMLGuard parses transaction parameters and performs argument parsing to recover cross-chain semantics, enabling seamless tracking across ledgers. Based on the inferred semantics, AMLGuard abstracts each transaction into a DeFi Semantic Unit (DSU). We evaluate the effectiveness of AMLGuard on 82 real-world laundering cases, involving illicit assets worth over $1 billion. Specifically, AMLGuard reconstructs compact illicit fund-flow topologies with destination precision of 94.4% and 87.6%, while achieving the highest address recall of 98.4% and 95.8% and destination recall of 94.1% and 93.8% on single-chain and cross-chain datasets.
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.
National currencies have long been associated with nation-state building and the expansion of state control. The rise of cryptocurrencies has the potential to disrupt state-society relations traditionally mediated through state-issued currencies. However, unregulated cryptocurrencies may be perceived as too unsafe to act as a true alternative to government-regulated currencies or investment vehicles. Cryptocurrency’s failures may instead lead people to appreciate the role of government more. Using the case of South Korea, we show that public discourse on cryptocurrencies has been more negative than positive in recent years. A demographically representative survey experiment reveals that exposing South Koreans to information about the volatility of cryptocurrencies increases their trust in government, as hypothesized. At the same time, exposure to positive information about cryptocurrencies does not undermine trust in government or support for government regulation. These results point to limitations of unregulated cryptocurrencies when it comes to eroding state-society relations.
Similar to all other cryptocurrency platforms, Ethereum is constantly confronted with malicious activities. In recent years, research efforts have targeted the detection and mitigation of malicious activities and the associated accounts within the Ethereum ecosystem. Yet, the malicious accounts represent only a small visible part of the substantial collaborative network enabling these activities. In this work, we offer the first analysis of this collaborative network and the corresponding affiliate accounts that often remain hidden from detection. We present enEtherShield, an enhanced framework for detecting affiliate accounts that assist malicious accounts in the related Ethereum scams. Our research findings lay the foundation for the detection of the collaborative network enabling Ethereum scams.
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.
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.