In near prospect, it is proposed to supplement the country's official reserves managed by state financial institutions with financial instruments created by private individuals in the form of cryptocurrencies. The purpose for this study was to carry out a comprehensive analysis for the goals, objective prerequisites, accumulated experience, as well as the real potential for further process development of including cryptocurrencies in the list of assets accepted as elements of national financial reserves. In the course of the study, the experience of a number of countries with different levels of socio-economic development was studied – from highly developed, leading in the global economy, to countries belonging to the economic periphery. The author notes the incompleteness and ambiguity of the consequences of the attempts to carry out such a bold monetary and financial transformation. The funding of completing the set of tasks set in the preparation of the presented scientific paper was the conclusion that there is an urgent need for a deep theoretical study of measures to balance central banks with financial assets that are decentralized in origin, such as cryptocurrencies, instead of an experiment that is not prepared scientifically, methodically and organizationally, which is expressed in the partial replacement of official reserves of fiat currencies with cryptocurrencies. At the empirical level, it seems advisable for the state to accumulate initially and use the latter in a specially created investment cryptocurrency fund.
This study substantiates blockchain analytics as a specialized expert tool for detecting the legalization of criminal proceeds under wartime conditions. The purpose is to systematize the methodological foundations of distributed ledger forensics and develop a conceptual model for its integration into Ukraine’s financial monitoring system. The implementation involves a comparative analysis of scholarly sources and a review of international regulatory standards in the field of anti-money laundering. Graph neural networks ensure an accuracy of 91 to 96 percent in detecting illicit transactions, and the dominant schemes for laundering wartime proceeds are sanctions arbitrage through stablecoins, fund mixing, and DeFi-based legalization through decentralized protocols. The immutability of records in the distributed ledger creates a unique evidentiary environment that enables retrospective analysis of transaction chains even after laundering operations have been completed. The findings confirm the necessity of fully implementing FATF Recommendation 15 and establishing specialized crypto-forensics units within the structure of domestic law enforcement agencies. The proposed four-level model, encompassing data collection, graph analysis, scheme identification, and evidence formation, defines a practical path toward standardizing crypto-forensics in domestic forensic expert practice and improving the effectiveness of financial investigations.
Open access
Business and Economic Development
Legal, Health, Environmental and COVID-19 Challenges
Miners of proof-of-work networks like Bitcoin tend to gravitate towards regions with cheap energy. We analyze risks associated with this geographical centralization by exploiting a local electricity supply shock. Compared to a control group consisting of an energy-efficient proof-of-stake cryptocurrency, the blockchain’s capacity for processing transactions decreases while transaction fees increase substantially. The increased settlement latency on the blockchain also reduces secondary market quality as seen in higher exchange rate volatility, lower liquidity, and larger price differences between exchanges. Overall, our results suggest that geographical centralization poses short-lived but potentially severe system-wide risks to proof-of-work networks.
We investigate the evolving structure of interactions in cryptocurrency markets using a network-based framework constructed from high-frequency price data spanning 2020-2025. Directed and weighted networks are constructed from statistically significant Granger causal relationships between cryptocurrency log-returns, enabling us to quantify the flow of influence across assets. We find that normalized returns exhibit heavy-tailed distributions, consistent with the presence of large intermittent fluctuations and in line with stylized facts of financial markets. The resulting networks display pronounced heterogeneity in link weights and nodal strengths, indicating that a small subset of cryptocurrencies contributes disproportionately to market dynamics. By ranking cryptocurrencies based on their nodal out-strength, we uncover a dynamically evolving hierarchy of influence. Ethereum consistently emerges as the most influential asset, while Bitcoin shows a gradual decline in its relative importance. The ranking structure exhibits substantial temporal variability, with multiple cryptocurrencies entering and exiting the top positions over time. Our findings reveal a highly competitive and non-stable organization of the cryptocurrency ecosystem.
Kanan Huseynov, Ali Shahzaib, István András Seres, János Tapolcai
From a user's perspective, perhaps the most significant difference between traditional banking services and widely used blockchain-based financial systems is that, in the latter, transactions and, either directly or indirectly, account balances and transaction histories are publicly observable. Therefore, a growing number of cryptographic solutions have been proposed to add a privacy layer to such systems. However, the privacy that users actually obtain does not depend solely on the security of the underlying cryptographic protocol: user behavior, transaction amount patterns, and timing decisions can substantially reduce anonymity. In this work, we study behavioral leakage in cryptocurrency mixers, focusing on Railgun on Ethereum. We aim to heuristically estimate the probability that a given deposit and withdrawal transaction belong to the same user. We consider five sources of leakage: characteristic timing patterns, address reuse, proximity in the transaction graph induced by prior public transactions, amount fingerprints that preserve distinctive digit patterns across transaction values, and knapsack type matches in which groups of transaction amounts add up in revealing ways. Our results show that even cryptographically strong privacy systems may suffer substantial anonymity loss due to user behavior and transaction patterns. Our five heuristics are able to uniquely link 17.65% of Railgun withdraw transactions to deposit transactions. We also applied a knapsack solver algorithm that was able to produce a 3.42 bit median anonymity loss for withdraw transactions. This work contributes to a better understanding of the practical privacy limits of mixers and anonymity pools, and points toward safer usage practices and design principles.
Cryptocurrencies have received long-term interest among investors because of the features of Bitcoin since its introduction in 2009. However, it is the same features that pose serious and diverse threats. These risks are very dangerous to the security of investors and the integrity of the market. Although their urgency is immense, there are very few systematic analyses that incorporate both regulatory and technological views. In this research, the mixed-method design is used, and an empirical investigation of high-profile security events is combined with the critical analysis of regulatory and technical literature in order to define, classify, and track the causes of the most widespread risks. The article explores the weaknesses and strengths of the existing laws and strategies that would curb identified risks that cryptocurrencies present. It also suggests practical and tangible solutions, which would make use of new technologies to minimize the damages and risks of cryptocurrencies to a greater extent. The analysis in this study proves that properly reducing risks should be performed in a two-faceted way; it should be done with the help of the regulation gaps in action and the utilization of new, protocol-infused technological limits. This study presents a moderate structure that is meant to achieve market security that does not suppress the dynamism and transparency of the cryptocurrency ecosystem. This study analyzes the problem of cryptocurrency security, financial regulation, blockchain technology, risk mitigation, and decentralized finance.
Anas Azenzoul, Nacer MAHOUAT, Ouissale El Gharbaoui, Jihane Tayazime · 6 authors
Tax systems worldwide face a compliance gap that OECD data places at USD 100–240 billion annually in corporate avoidance alone, before accounting for the shadow economy and crypto-asset transactions. FinTech mandatory e-invoicing, real-time transaction matching, and machine-learning audit selection is narrowing the informational conditions that enable evasion, while simultaneously introducing governance risks: opaque algorithmic audit targeting, contested blockchain forensic evidence, and the surveillance potential of programmable money. This article presents a PRISMA 2020 systematic literature review of 59 peer-reviewed articles (Scopus, Web of Science, and ScienceDirect), complemented by IRAMUTEQ lexicometric analysis and an extension of the Allingham Sandmo compliance model to incorporate algorithmic detection probabilities, bomb-crater belief dynamics, and Zero-Knowledge Proof verification. Four thematic clusters emerge: tax compliance behaviour and FinTech adoption (19.92%), digital transformation and corporate performance (35.34%), bibliometric and emerging-technology research (16.54%), and cryptocurrency markets and regulatory challenges (28.20%). Across them, FinTech reduces evasion where institutional and technical conditions allow but generates distributional, evidentiary, and constitutional risks that existing legal frameworks have yet to resolve. In response, we propose the Techno-Legal Due Process Framework (TLDPF) three pillars (Techno-Proportionality, Cryptographic Burden of Proof, and Algorithmic Constitutionalism) grounded in EU/OECD constitutional doctrine as a normative design proposal awaiting empirical validation.
This PhD thesis examines risks, opportunities and socio-technical innovation in blockchain-based financial systems, combining network analysis, empirical market data, and institutional analysis. As the crypto ecosystem and decentralized financial infrastructures continue to expand and interact with traditional monetary systems, understanding how risk propagates across assets, platforms, and institutional designs has become increasingly important for market participants and policymakers. The first two chapters focus on systemic risk in crypto assets (cryptocurrencies and stablecoins) using a network-based approach. The first paper analyzes major crypto assets and constructs dynamic networks based on return co-movements to study the evolution of interconnectedness and contagion risk over time. Network centrality measures (degree, closeness, betweenness, and eigenvector) are used to identify systemically important nodes (cryptocurrencies and stablecoins) and to assess how these measures affect their systemic risk contributions, particularly during market stress episodes. Results showed that the systemic risk contribution of crypto assets decreases over time as their connectedness in the system increases. This impact is more pronounced for cryptocurrencies than for centrally issued, managed, and governed stablecoins. Our findings suggest that pure network interconnectedness plays a diminished role in tail risk propagation in the crypto market. The second paper extends this framework to token pairs traded on centralized and decentralized exchanges (CEXs and DEXs), allowing for a comparison of market structure and risk transmission across trading platforms. By incorporating data from CEXs and DEXs, this chapter highlights differences in network topology and the role of liquidity concentration in shaping systemic risk. Results showed that centrality values significantly impact systemic risk contribution of token pairs listed on centralized exchanges. Conversely, insignificant results were found for all token pairs traded on decentralized exchanges. The token pairs on centralized exchanges exhibited a negative association with centrality values, consistent with the findings reported in the first paper. These findings imply that systemic risk in cryptocurrency markets is not solely driven by interconnectedness, but by how that interconnectedness is structured. In particular, the negative relationship between centrality and systemic risk suggests that higher network integration, supported by transparency and decentralized architectures, may enhance risk sharing and reduce systemic vulnerability. These results highlight the potential of blockchain based financial systems to contribute to more resilient, efficient, and inclusive financial ecosystems, while also offering new insights for the design of risk management and regulatory frameworks. The third paper shifts the focus from market level risk to protocol level risk management in leading Decentralized Finance (DeFi) lending platforms. It examines the determinants of liquidation events and evaluates the effectiveness of protocol design features as risk management tools. Exploiting the transition from earlier to newer protocol versions across different blockchain layers, the empirical analysis employs panel fixed effect regression models to assess how changes in risk control measures 3 affect liquidation dynamics and protocol’s performance. The findings emphasize that protocol level design choices play a critical role in mitigating risk beyond asset price volatility alone. The architectural evolution from v2 to v3, characterized by granular risk parameters, isolation modes, and enhanced risk management mechanisms has systematically improved protocol resilience, with liquidations in v3 serving as positive signals of stability rather than distress. The fourth paper broadens the scope of the thesis by examining blockchain based complementary currencies in comparison with traditional complementary currency systems, with a particular focus on their potential role in universal basic income schemes. It investigates the socio-technical evolution of Complementary Currencies for Basic Income using a data-driven approach to different case studies (Fiat and Blockchain based models). It highlights how technological choices influence scalability, transparency, and risk exposure in social and monetary innovations by employing mix method approach. Finally, based on the trade-offs of each system, a hybrid model for UBI is proposed for financial inclusion and poverty elimination. Taken together, the four papers provide an integrated perspective on risks and opportunities in emerging financial ecosystems, spanning asset markets, trading infrastructure, decentralized protocols, and alternative monetary arrangements. Overall, the results suggest that the core features of blockchain based markets, e.g., decentralization, transparency, accessibility, low transaction costs and automated risk management, are not merely technological innovations but may serve as mechanisms for improving system resilience and inclusive financial architectures. This thesis also contributes to the literature by demonstrating how network structures and institutional design jointly shape systemic risk and resilience in DeFi, offering insights relevant for researchers, protocol designers, and policymakers navigating the evolving digital financial landscape.
In the context of developments in the field of financial technology, cryptocurrencies, emerging as a new asset class, have garnered significant attention in financial markets in recent years, attracting investors, researchers, and regulators, and leading to numerous publications. Bibliometric studies evaluate these publications based on criteria such as the number of publications, their quality, the countries of publication, authors, and journals. This study aims to perform a bibliometric analysis of the academic literature available in the Web of Science (WoS) database, focusing on the volatility of cryptocurrency prices. It analyzes the magnitude and development of academic interest in this field, along with key words, the most cited works, and research trends, in an effort to determine the density of studies, their impact areas, and the academic networks that have emerged in this field. Based on the general findings, it is observed that the number of studies has been on an increasing trend over the years, and that the publications are predominantly in the field of Business Economics. Moreover, it has been found that publications are mainly in finance journals. In terms of network maps, the findings suggest a moderate level of collaboration among authors, with the United Kingdom and the People's Republic of China occupying central positions in international collaboration. In terms of citations, authors such as Lucey, and Katsiampa, Paraskevi, have emerged as prominent figures in the fields of cryptocurrencies and volatility. Regarding key words, terms like 'cryptocurrency', 'cryptocurrencies', 'volatility', and 'bitcoin' are predominantly used in these studies." Keywords: cryptocurrencies, bitcoin, volatility, bibliometric analysis
This study compares the forecasting performance of four deep learning architectures—GRU, LSTM, RNN, and CNN—for one-step-ahead Bitcoin price prediction. A grid search determined the optimal configuration, which was applied uniformly across models to ensure fair evaluation. Using daily BTC closing prices from January 2018 to July 2025, it is found that the GRU model achieved the lowest forecasting errors (MSE, RMSE, MAE, MAPE) and the highest R², with LSTM performing closely behind. Visual analyses confirmed that GRU and LSTM maintained stronger alignment with actual prices during volatile periods. To assess economic value, model forecasts were integrated into a rule-based trading strategy under realistic market frictions, including a 0.10% transaction cost and a 0.10% trading threshold, with both short-selling-enabled and long-only variants tested. The GRU strategy with short-selling generated the highest terminal wealth (approximately 24% higher than the Buy-and-Hold benchmark) and superior risk-adjusted returns, measured by CAGR, Maximum Drawdown, and Sharpe Ratio. The findings demonstrate that careful hyperparameter optimization, coupled with an architecture capable of capturing complex temporal dependencies, can significantly improve both predictive accuracy and trading profitability in cryptocurrency markets. These results provide practical implications for designing AI-driven trading systems.
Collective dynamics in financial markets can emerge through synchronized movements of large groups of assets. Motivated by analogies with interacting many-body systems, we introduce a spin-lattice representation for analyzing collective states in cryptocurrency markets. In this framework, assets are encoded as binary spin variables according to the sign of their returns, while correlations between assets determine effective interaction strengths. A correlation-based breadth-first search (CBFS) procedure embeds 169 cryptocurrencies into a $13 \times 13$ lattice, enabling the construction of an Ising-like Hamiltonian describing the market configuration, which we call the \emph{Market Crystal}. Macroscopic observables such as magnetization and energy provide a statistical-mechanical characterization of collective market states. The resulting phase-space structure highlights regimes of strong alignment and fragmentation among assets, with an energy--magnetization pattern suggestive of predominantly ferromagnetic interactions. This framework offers a statistical-mechanical viewpoint for studying collective behavior in financial systems.
Antonio Pellicani, Gianvito Pio, Sašo Džeroski, Michelangelo Ceci
Abstract The cryptocurrency market represents a significant innovation in the financial ecosystem, built upon cryptographic principles to ensure secure and transparent transactions. Cryptocurrencies experienced a global adoption, driven by their decentralized nature that enables borderless transactions without third-party intermediaries. The price of cryptocurrencies is characterized by a significant volatility, that introduces both opportunities and challenges. In this context, the development of accurate methods for the forecasting of price variation, able to work in real-time on data streams, has become vital for various stakeholders. In this paper, we propose a novel approach, called LEMON, for the online prediction of the price variation of cryptocurrencies, that leverages possible temporal correlations among them. Our approach stems from the empirical evidence that cryptocurrencies tend to form groups characterized by similar trends, a behavior often attributed to shared market dynamics and common external factors. Through the analysis of temporal correlations, LEMON dynamically identifies these groups, that are then exploited to learn multiple multi-target tree-based models, specifically designed for processing continuous data streams. LEMON also introduces a novel adaptive non-parametric weighting scheme, that automatically adjusts the importance of each instance based on the observed data distribution in real-time, improving the forecasting of the price variation. Our experiments, performed on 16 datasets related to 16 cryptocurrencies, demonstrate that LEMON outperforms state-of-the-art approaches in two distinct prediction tasks: forecasting the closing price variation (regression) and predicting the market trend direction (classification), making it an effective tool to support stakeholders requiring accurate real-time predictions.
Objective: This study examines the legal and procedural challenges posed by decentralised finance (DeFi) technologies to the anti-money laundering framework in Iraq, The research problem lies in the clear regulatory gap resulting from the decentralised nature of these platforms, which relies on smart contract technology and blockchain to eliminate the need for traditional financial intermediaries; this decentralised nature hinders the ability of Iraq’s Anti-Money Laundering and Counter-Terrorist Financing Law No. 39 of 2015 to control cryptocurrency flows and establish criminal liability in this context,، Method: The study adopted a comparative analytical approach, analysing the text of Iraqi legislation and comparing it with the operating mechanisms of decentralised finance platforms, whilst also examining the extent to which it complies with the updated international standards issued by the Financial Action Task Force (FATF) In particular, with regard to Recommendation No. 15, Results: the study reached a number of important conclusions, the most notable of which is that the current legal definitions of funds and financial institutions in Iraq are outdated, thereby limiting the ability of regulatory bodies to track virtual assets, Novelty: The study also identified procedural shortcomings in the handling of encrypted digital evidence and recommended urgent legislative reforms, including the regulation and oversight of Virtual Asset Service Providers (VASPs) through the establishment of a dedicated institutional framework.
Penelitian ini bertujuan untuk mengevaluasi penggunaan cryptocurrency sebagai metode pembayaran zakat yang halal di Malaysia, dengan mengeksplorasi kesesuaiannya dengan prinsip-prinsip Islam sambil mempertimbangkan inovasi teknologi dan kepatuhan syariah untuk pembayaran zakat yang efisien dan transparan. Pendekatan kualitatif digunakan, melibatkan tinjauan pustaka dan analisis regulasi terkait fatwa Malaysia, peraturan keuangan, serta sumber akademis tentang keuangan Islam dan cryptocurrency. Cryptocurrency dapat berfungsi sebagai sarana halal untuk zakat jika memenuhi kriteria syariah seperti transparansi, kepemilikan aset yang sah, serta menghindari gharar dan riba. Regulasi dan fatwa di Malaysia menunjukkan penerimaan yang berkembang di bawah pengawasan ketat; teknologi blockchain meningkatkan akuntabilitas distribusi zakat, meskipun volatilitas nilai dan pemahaman publik tetap menjadi tantangan utama. Integrasi cryptocurrency dapat memodernisasi sistem zakat, meningkatkan kepercayaan dan transparansi sekaligus memastikan kepatuhan syariah. Kolaborasi antara regulator, ulama Islam, dan pengembang fintech sangat penting untuk membangun ekosistem zakat digital yang inklusif dan dapat diakses oleh komunitas Muslim Malaysia. Studi ini menawarkan perspektif inovatif dengan menggabungkan analisis regulasi, teknologi, dan fiqh mengenai cryptocurrency halal untuk zakat di Malaysia, mengisi kekosongan penelitian tentang solusi keuangan Islam digital di pasar negara berkembang.
The digital revolution has spawned new assets such as cryptocurrency, non-fungible tokens (NFTs), monetized accounts, and digital estates that are increasingly dominant in the Indonesian economy; however, these inheritance objects have not been explicitly addressed by classical fiqih mīrāth provisions or the Compilation of Islamic Law (KHI), creating a legal vacuum that threatens legal certainty and the protection of heirs' rights. This study aims to reconstruct inheritance fiqih regarding digital assets and cryptocurrency within the perspective of Indonesian Islamic Family Law to ensure proportional and equitable protection of heirs' rights. Employing a normative-empirical legal research method with a conceptual approach, maqāṣid asy-syari'ah, and juridical-empirical analysis of religious court decisions from 2020–2025 as well as in-depth interviews with judges and practitioners, this research analyzes the concept of māl in fiqih and judicial practice. The results indicate that digital assets fulfill the pillars of māl functionally (manfa'ah, taṣarruf, hifẓ); however, judicial practice remains trapped in three inconsistent patterns avoidance, proportional inclusion, and expert-assisted valuation which systematically threaten the rights of female and child heirs due to the absence of valuation guidelines and private key escrow mechanisms. This study formulates a new fiqih maxim based on ḥifẓ al-māl and ḥifẓ an-nasl and proposes a digital estate declaration to guarantee legal certainty and equitable distribution. This original contribution expands the frontier of contemporary ushul fiqih by introducing a digital māl taxonomy in Islamic inheritance and opens an interdisciplinary discourse on Islamic family law, fintech, and blockchain..
The advent of decentralized cryptocurrencies has reignited fundamental debates in monetary economics about the nature and future of money. Proponents of digital currencies argue that decentralized, algorithmically governed assets can supplant central banks in managing monetary conditions and stabilizing economic outcomes. This chapter critically examines this proposition by evaluating cryptocurrencies against the classical functions of money and the core instruments of monetary policy. Grounded in monetary theory – from Friedman’s monetarism and Mises’ Austrian framework to Modern Monetary Theory – and extended through a behavioral finance lens, the analysis reveals that widespread belief in cryptocurrency as a viable monetary policy alternative is driven not merely by technological innovation but by deeply embedded cognitive biases, including overconfidence, narrative-driven speculation, and institutional distrust. The chapter also treats money as an economic asset subject to market competition. Drawing on Austrian economic theory and classical competition principles, the analysis evaluates whether decentralized currencies can realistically compete with sovereign money in an open monetary market. By integrating monetary economics with strategic competition frameworks, the chapter explores whether cryptocurrencies can achieve monetary dominance through efficiency, cost advantages, or differentiated value propositions. Based on principles from strategic business theories such as differentiation and cost-leadership, the chapter treats money as a competitive good subject to market dynamics, ultimately concluding that while cryptocurrencies represent a significant financial innovation, they fundamentally lack the institutional architecture and behavioral predictability required to replace central bank monetary policy.
The digitization of financial markets has produced two classes of platforms that price, in principle, the same state - contingent payoffs: centralized crypto-option exchanges and blockchain-based prediction markets. This paper provides the first option-implied benchmark test of prediction-market pricing for cryptocurrency threshold contracts. For each hour in a matched sample, we compare the Polymarket Yes price with the discounted risk-neutral binary value implied by a listed Binance call option on the same underlying, strike, and maturity, and study the gap between them. In the main September 2023 Bitcoin contract, the mean pricing gap equals 5.6 percentage points across 214 hourly observations (t = 6.46, p < 10^{-9}). Pooling three Binance-compatible Bitcoin threshold markets yields a mean gap of 6.3 percentage points across 287 observations, robust to HAC and block-bootstrap inference. The gap is persistent - with an AR(1) half-life of roughly four hours - yet mean-reverting, consistent with slow information transmission between segmented venues rather than mechanical noise. Cross-sectional regressions reveal that the wedge is largest at low option-implied probabilities and long maturities, a pattern consistent with speculative demand for prediction-market contracts rather than measurement error. A delta-hedged arbitrage proxy remains profitable after conservative transaction costs, though with marginal statistical precision. A Deribit extension on the same three Bitcoin contracts produces a larger pooled gap of 11 percentage points, while a smaller Ethereum exercise yields mixed evidence. The results demonstrate that digital fragmentation of financial markets generates systematic, persistent pricing wedges even for economically identical payoffs.
Daniel Pereira Alves de Abreu, Octávio Valente Campos, Aureliano Angel Bressan
Objective: This study aims to evaluate the performance of different ARMA-GARCH model specifications in the risk management of major cryptocurrencies, investigating whether the inclusion of exogenous variables improves the calibration of risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). Methodology: To achieve this objective, 4,032 specifications of the ARMA-GARCH model applied to the ten main cryptocurrencies in trading were tested. The study incorporated the Fear and Greed Index and Bitcoin Trading Volume as exogenous variables in an ARMA-GARCH-X framework, comparing the performance of the different specifications against an ARMA(1,1)-GARCH(1,1) benchmark. Originality: Despite growing interest in crypto asset risk management, there are still gaps in the literature regarding the effectiveness of incorporating exogenous variables into forecasting models, as well as the increase in the quality of forecasts when using more complex models. Main results: The results indicate that the inclusion of external variables improves risk calibration in some assets, although the gains are marginal and heterogeneous. There is also no single optimal parameterization, requiring ARMA orders, GARCH specifications, and error distributions to be adjusted for each cryptocurrency. Theoretical/methodological contributions: From a methodological point of view, the study contributes by demonstrating the importance of specific calibration of ARMA-GARCH models for different cryptocurrencies in risk estimation. Furthermore, the results suggest that, although more complex models can improve tail risk estimation, the gains in predictive power over simpler models are limited. Keywords: Cryptocurrencies; Risk Management; ARMA-GARCH; Value-at-Risk; Expected Shortfall.
This chapter delves into the evolving landscape of cryptocurrencies and digital assets, offering a comprehensive understanding of their foundations, functions, and financial implications. It begins by distinguishing between cryptocurrencies and stablecoins, unpacking their technological frame-works, value mechanisms, and economic roles within the broader digital finance ecosystem. As decentralized currencies gain mainstream traction, the chapter critically examines the legal and regulatory complexities that differ widely across jurisdictions – highlighting challenges such as investor protection, anti-money laundering (AML) compliance, and central bank policies. Furthermore, the chapter explores the behavioral economics of crypto investors, shedding light on psychological drivers like speculation, herd behavior, and risk perception. Various valuation models, including network value-to-transactions (NVT) and sentiment analysis, are discussed to understand how digital assets are priced in volatile and often opaque markets. Lastly, the chapter evaluates the risks and opportunities of investing in digital assets, balancing concerns over security breaches, market manipulation, and regulatory uncertainty with the potential for high returns, diversification, and financial democratization. Through this multidimensional lens, the chapter equips readers with the analytical tools and critical perspective necessary to navigate the dynamic world of digital finance.
Alexandra Conda, Ștefan Găman, Raul Cristian Bag, Miruna Mazurencu-Marinescu-Pele · 6 authors
Abstract This study investigates the relationship between Facebook sentiment and Bitcoin market dynamics using AI-based emotion detection. We analyze 120,000 Facebook posts collected via CrowdTangle alongside Bitcoin financial data from the Blockchain Research Center, covering 2015–2023. Employing FinBERT for sentiment classification, we develop novel compound sentiment scores that integrate text-based sentiment with Facebook’s multi-reaction engagement system, then apply four analytical components: sentiment analysis, Dynamic Topic Modeling, sentiment-based trading strategies, and machine learning volume prediction. Results demonstrate that Facebook sentiment has substantial predictive power for Bitcoin trading volume. Sentiment-based trading strategies significantly outperform buy-and-hold, achieving superior cumulative returns and risk-adjusted performance. For volume prediction, Linear Regression and Bidirectional LSTM achieve comparable test performance, indicating that model complexity does not guarantee superior prediction. Topic modeling reveals that cryptocurrency investment and trading discussions dominate Bitcoin discourse on Facebook, with themes evolving over time in response to market conditions. This research contributes by being the first to apply post-level NLP sentiment analysis of Facebook data to cryptocurrency markets, extending beyond the Twitter and Reddit focus of prior research. The findings provide practical tools for traders and analysts navigating volatile digital asset markets while demonstrating that Facebook’s demographically diverse user base and rich reaction system offer unique advantages for sentiment quantification.
A current, urgent problem is whether the price behavior pattern of significant quantities of digital assets reflects a single direction trend line or multiple phases that exhibit different structures, adjusted inter-asset relationship differences, and changes in management systems, given the growing importance of digital assets in investment portfolios and collateral holdings, exchange-traded funds (ETFs), new forms of financial activities, and system risks over the period from 2020 through 2025. Because of this period’s post-pandemic recovery, speculative overextension, sharp decline, stabilization, and the re-entry of large-scale institutions into practice, these changes in prices are more clearly identified under such a context. Empirically, this study integrates descriptive statistics, rolling volatility analysis, augmented Dickey–Fuller’s unit-root test, segmented trend regression model with structural breaks, and vector autoregression (VAR) for return interactions. Based on these bases, both Bitcoin and Ethereum have demonstrated a relatively strong direction of continuous appreciation, together with quite considerable regime-specific instability. The log-price series is non-stationary, but the daily return series is stationary; so a level model is appropriate for medium-term trend analysis, and returns-based models can be applied more flexibly at shorter timespans. The segmented trend-regression analysis shows that close to peaks, such as those that occurred in 2021 for a long period, the 2022 correction, and the resumption of investment in 2024, are relatively distinct from the overall linear change pattern across all time periods. Both Bitcoin and Ethereum display pronounced contemporaneous co-movement, but they show no substantial lags via VAR or Granger causality tests conducted in the context of time-varying parameters. This study employs an integrated empirical research approach based on various perspectives to explore the long-term structural adjustment and near-instantaneous cross-market relationship dynamics, as well as regulatory mechanisms within a systemic context.