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.
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.
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.
Francesco Cesarone, Gianna Figà‐Talamanca, Francesca Luciani
Abstract This study develops a large-scale framework to evaluate whether, and under what conditions, adding cryptocurrencies to equity investment universes improves portfolio performance.We apply four long-only portfolio strategies, Global Minimum Variance, Risk Parity, Most Diversified Portfolio, and Equally Weighted, to 10,000 randomly generated investment universes. These universes consist of baskets containing either only equities or varying combinations of equities and cryptocurrencies. We conduct an out-of-sample analysis on real-world data from 2018 to 2023 to assess the influence of cryptocurrencies on portfolio outcomes. The empirical findings reveal that portfolios constructed from mixed equity and cryptocurrency universes provide a better risk-return profile compared to purely equity-based portfolios, particularly for Risk Parity, Most Diversified, and Equally Weighted.
As cryptocurrency is increasingly adopted, regulators must consider whether regulations are needed to protect investors and consumers. In prior research involving a behavioral experiment, we identified the existence of a face value effect when people use cryptocurrency in transactions. Just as prior researchers have found a face value effect when people use foreign cryptocurrency,we found a face value effect with the use of cryptocurrency. People predictably anchor on the nominal amount indicated by the cryptocurrency and fail to accurately convert the amount into their home currency. This cognitive bias results in significant overspending when the cryptocurrency is stronger than the U.S. dollar (USD). This Article examined whether different interventions could reduce this cognitive bias. Based on the results of another behavioral experiment we conducted, we found two interventions did so. First, when prices for a transaction are displayed in both USD and cryptocurrency values, the face value effect and overspending were mitigated. Second, in situations involving bidding on an item with no fixed price, requiring people to write out their bid or payment in USD before bidding in cryptocurrency was even more effective in reducing the face value effect and overspending. Accordingly, we propose the adoption of (1) domestic currency pricing (DCP) for items sold in cryptocurrency that requires the corresponding USD amount to be included for any price in cryptocurrency; and (2) for bidding on items in cryptocurrency, a simple requirement for people to “type out the price” of their bid first in USD, or the “TOP price” for short. These interventions are modest, but they may help reduce unintended overspending due to the face value effect.
The rapid expansion of U.S. financial technology platforms has created new vectors for money laundering, terrorist financing and financial crime that traditional anti-money laundering frameworks were not designed to address. This article presents a systematic literature review of 78 peer-reviewed studies published between 2015 and 2025 to examine the design, performance and policy implications of advanced anti-money laundering frameworks for U.S. fintech platforms. This study draws on evidence from financial criminology, regulatory law, computer science and organizational studies; the review finds that machine learning-based transaction monitoring systems reduce false positive alert rates by 40 to 70 percent compared to rule-based systems, as well as improving detection of sophisticated layering schemes. Blockchain analytics tools partially de-anonymize cryptocurrency transaction flows and have been used to identify illicit financial activity on major blockchain networks. Regulatory technology platforms automate suspicious activity reporting, beneficial ownership identification and customer due diligence workflows in ways that reduce compliance costs as well as improve regulatory data quality. However, the reviewed literature also documents persistent challenges, including algorithmic disparate impact in AML monitoring systems, beneficial ownership opacity through shell company structures, regulatory arbitrage between licensed exchanges and decentralized finance protocols and the systemic underutilization of suspicious activity report intelligence by law enforcement agencies. The article concludes with six evidence-based policy recommendations and a research agenda for advancing AML framework effectiveness in the rapidly evolving U.S. fintech sector. Keywords: Anti-Money Laundering, Fintech, AML Compliance, Machine Learning, Transaction Monitoring, Know Your Customer, Cryptocurrency Regulation, Regulatory Technology, Suspicious Activity Reporting, Financial Crime.
Jakub Zwydak, Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
Artificial transaction generation remains an important source of potential market manipulation on cryptocurrency exchanges, as it may distort reported liquidity and reduce market transparency. This study proposes a diagnostic framework for detecting unusual trading patterns based on complexity and statistical-structure measures derived from high-frequency trade-level data. The analysis considers log-returns, trading volume, and transaction counts, using tail distributions, autocorrelation functions, multifractal characteristics, approximate entropy, and detrended cross-correlations. The methodology is applied to BTC, ETH, and XRP traded on Binance, Bitget, KuCoin, and Kraken over the period from April 1 to June 30, 2025. The results reveal a pronounced anomaly on Bitget for BTC and ETH after mid-May 2025. The number of transactions increases sharply, but there is no proportional increase in traded volume or return fluctuations. This regime is characterised by numerous low-volume trades, weaker autocorrelations, reduced multifractal organisation, higher short-pattern irregularity, and weaker cross-correlations involving the transaction-count series. These features are consistent with a noise-like component in trading activity and may indicate artificially increased transaction counts, although they do not provide direct proof of wash trading. The findings show that complexity-based indicators can be useful for detecting exchange-specific trading anomalies that remain hidden in price-based measures.
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.
Cryptocurrency markets are vulnerable to trade-based manipulation, such as wash trading, which can distort price signals and mislead investors. Prior research has mainly focused on detecting manipulation using fixed rules or labeled examples, offering limited flexibility and interpretability for assessing potential risks. Existing visual analytics tools can reveal basic manipulation-related signals, such as token distribution, but still require substantial manual effort to integrate holder relationships, suspicious behaviors, and market dynamics for risk assessment. To address these limitations, we propose ManiScope, an LLM-assisted visual analytics system for analyzing trade-based manipulation risks in cryptocurrency markets. ManiScope provides coordinated views of token distributions, holder relationships, detailed holder behaviors, price dynamics, and suspicious trading patterns. To further enhance user analysis, ManiScope introduces a human-LLM collaborative visual analytics framework. Rather than acting as a basic reactive LLM assistant, the framework positions the LLM as a co-analyst that infers users' analytical intent and emerging hypotheses from interaction context and surfaces relevant visual, statistical, and synthesized evidence for hypothesis evaluation. This design reduces repetitive inspection and strengthens evidence-based reasoning. We evaluate ManiScope through two case studies and a user study with 12 experienced cryptocurrency practitioners. The results suggest that ManiScope supports effective risk assessment of manipulation, reduces manual effort in evidence-seeking, and organizes findings around user hypotheses.
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.
Ignatia Bintang Filia Dei Susilo, Vidya Purnamasari, Sulistya Rini Pratiwi, Yelly Zamaya · 5 authors
The rapid development of smart-contract-based blockchain ecosystems has transformed the perception of digital assets. However, the extent to which these assets are influenced by macroeconomic conditions in emerging markets remains poorly understood. This study aims to examine the long-term and short-term relationships between three major smart-contract platforms: Ethereum (ETH), Build and Build (BNB) Chain, and Solana (SOL), and several Indonesian macroeconomic indicators: money supply (M2), consumer price index (CPI), the rupiah-to-US dollar exchange rate (IDR/USD), and the policy interest rate (BI Rate). The study draws on monthly data spanning April 2023 to September 2025. The findings reveal that each platform exhibits a distinct degree of sensitivity to Indonesian macroeconomic conditions. Overall, the three platforms demonstrate a strong long-run relationship with the selected macroeconomic variables. The rising money supply (M2) tends to have a positive effect on all three platforms, while the influence of the exchange rate varies across ecosystems. Furthermore, this study traces how shocks in macroeconomic variables are transmitted to cryptocurrency prices and identifies distinct volatility patterns across the three platforms. Its findings contribute to understanding the relationship between crypto assets and macroeconomic conditions. It also offers practical insights for portfolio diversification strategies and for developing regulatory frameworks in Indonesia's growing digital asset market.
This study examines the regulation of blockchain technology and cryptocurrencies in Morocco’s foreign exchange market, focusing on the challenges posed by restrictive regulatory frameworks and their implications for financial stability. Although cryptocurrency transactions have been officially prohibited since 2017, their use has continued to expand through informal and peer-to-peer channels, raising concerns about the effectiveness of prohibition-based regulation. Using a systematic literature review based on the PRISMA framework, this study analyzes academic publications, institutional reports, and international regulatory developments covering the period 2018–2026. The findings reveal a persistent mismatch between formal regulation and actual market practices, resulting in regulatory arbitrage, weak enforcement, and the expansion of informal cryptocurrency activities. The analysis further highlights significant macro-financial risks, including capital flight, exchange rate pressures, and reduced monetary policy effectiveness. By explicitly linking cryptocurrency regulation to foreign exchange market dynamics in an emerging economy, the study addresses an underexplored area in the literature. It concludes that Morocco’s current restrictive approach is unlikely to remain effective and argues for the adoption of a more adaptive, risk-based regulatory framework capable of promoting financial innovation while preserving macroeconomic stability and regulatory oversight.
Centralized cryptocurrency exchanges (CEXes) enable fast off-chain conversions between hundreds of coins. It is an open question which algorithmic trading patterns occur on these platforms. A major challenge to measuring CEXes is that their public trade data does not contain addresses or trader identifiers allowing linkage. We propose a novel methodology to infer one-way arbitrage (OWA) trading in anonymized spot trade data from CEXes. We identify 402 M likely OWA sequences in 5 years of trading on Binance (and almost 2 M during 9 years on Kraken), accounting for 0.94 % and 0.13 % of the total traded volume, respectively. While we estimate total profits of $31.2 M on Binance and $975 k on Kraken, profits from individual OWA sequences are less than $1 on average after accounting for trading fees. We also observe that OWA has become faster over time, while the profitability of individual sequences has decreased. Our findings highlight that pricing discrepancies regularly occur in CEXes, and raise questions for future work to identify the precise circumstances that enable profitable OWA.
Cryptocurrency markets exhibit periodic bursts in volatility and volume at one-minute, five-minute, and quarter-hour marks. Using trade data for six Binance perpetual contracts, we link these bursts to algorithmic participation: trade-size roundness declines sharply during them. The Autocorrelation Map, a clock-phase-resolved display, reveals serial dependence in order flow and returns at quarter-hour openings that conventional measures obscure. Opening returns are predictable out of sample, while opening order imbalance predicts returns over four to twelve hours, with much weaker effects at finer clock-time frequencies. Together, these findings characterize periodic algorithmic trading and its cross-frequency variation.
Discover how Tokemak is revolutionizing decentralized finance through its advanced liquidity management solution. Get insight into the protocol's mechanism, governance structure, token purpose, and the significance of its emergence in the DeFi world.
To make the payment system robust and user friendly, decentralized based Scan and Pay system need to be designed. This paper integrates the Unified Payments Interface (UPI) of India with the Solana-based Blockchain to make the payment system decentralized. Solana offers a high throughput and low-cost based decentralized infrastructure which is combined with the simple and reliable UPI system. So, the proposed system enables cryptocurrency transactions linked to UPI while maintaining user friendliness, scalability, and regulatory compliance. The designed method uses a secure architecture powered by smart contracts and modular design. It offers a viable bridge between centralized financial networks and emerging Web3 ecosystems. Proposed Solana-based UPI is compared with the Non-Solana based UPI which is using Blockchain. Results show that there is improvement of 91% in transaction latency and 95% in transaction cost as compared to the Non-Solana based UPI system.
Existing cybercrime classification schemas capture contact metadata and financial transactions but omit the psychological manipulation techniques perpetrators employ. We present a forensic schema (four categories, 35 questions) adding 11 manipulation indicators and cryptocurrency evidence fields to established forensic foundations. Applied to 10,994 victim reports via large language model (LLM)-driven annotation and validated against two human annotators (mean LLM-human $κ= 0.69$, matching inter-annotator $κ= 0.68$), the schema revealed a statistically distinct manipulation profile for each major fraud type (Cramer's $V$ up to $0.790$). A rationale-based evidence audit nonetheless exposed a forensic detail gap: detection of manipulation techniques was reliable, but victim narratives varied widely in the actionable detail supporting each Yes answer, and blockchain-specific identifiers were nearly absent. These findings point to AI-assisted victim intake with schema-informed follow-up questions as the most direct way to close the gap. The tiered annotation strategy also provides a reusable template for LLM-based extraction from other forensic text domains.
Monero is a privacy-focused cryptocurrency that deploys the Dandelion++ protocol and incorporates anonymity networks (such as Tor and I2P) to prevent malicious attackers from linking transactions with their source IPs. In this paper, we demonstrate that Monero's integration of the Tor network introduces a fundamental vulnerability: a Monero Tor node's originated transactions are exclusively forwarded to two outgoing Tor hidden service nodes (proxy nodes) prior to clearnet propagation, enabling an adversary to capture originated transactions by occupying the target node's outgoing connections. Based on this observation, we propose \textit{ProxyMark}, a three-stage deanonymization framework for the Monero Tor network, comprising node role identification, originated transaction identification, and node location deanonymization. Through experiments on the live Tor network, Monero mainnet, and testnet, we empirically demonstrate the effectiveness of \textit{ProxyMark} in successfully deanonymizing transactions originating from Monero nodes over Tor.
Zero-knowledge privacy protocols let users hide transaction details on public blockchains. Systems like Tornado Cash, FixedFloat, and the Houdini Private Swap feature recently added to Jumper rely on cryptographic techniques that unlink sender and receiver addresses. These constructions give legitimate users meaningful protection for their financial activity. They also create a straightforward dual-use dilemma. The February 2025 Bybit incident supplies a clear example. Thieves stole $1.5 billion in ETH, the largest cryptocurrency theft on record. The FBI linked the attack to North Korea’s Lazarus Group. The stolen funds moved quickly through Tornado Cash. The resulting lack of transparency triggered a wave of customer withdrawals. Bybit responded by securing loans of several hundred million dollars from other institutions to keep its operations running. Cases like this demonstrate that zk-based privacy tools, when used at large scale for illicit purposes, can accelerate liquidity crises and place costs on market participants who had no involvement in the original theft. The real problem is not the underlying mathematics that delivers privacy. It lies in the missing mechanisms that could impose accountability on criminal actors while leaving the privacy protections for everyone else intact.
The rapid integration of cryptocurrencies into mainstream finance has introduced a novel form of digital collateral into the mortgage lending landscape, yet the consequences for traditional real estate-backed lending applications remain poorly understood. Integrating the Technology–Organization–Environment (TOE) framework with the core-satellite investment model, this study investigates if crypto-backed products displace conventional real estate-backed applications. Utilizing traditional mortgage application records from a financial institution adopting crypto-collateral in 2022, we find that crypto-backed mortgages significantly reduce traditional mortgage approval rates. This "crowding out" effect is intensified by regional cryptocurrency legitimacy and housing illiquidity, while mitigated by property information insensitivity and speculative concerns. Furthermore, results reveal heterogeneous impacts across demographics: this innovation potentially exacerbates racial discrimination while alleviating age-based disparities. These findings underscore the complex interactions between emerging fintech and traditional mortgage lending, suggesting that collateral innovation may redistribute credit access across diverse market segments.