Search Results (46)

The acceleration of financial innovation and pro-crypto regulations in the digital asset space have spurred interest in cryptocurrencies among funds, and institutional and retail investors. Like any risky assets, investment in digital assets offers opportunities in terms of returns and challenges in terms of risk. However, unlike traditional assets, digital assets like cryptocurrencies are highly volatile. Accordingly, applying conventional single-criterion financial metrics for portfolio construction may not be sufficient as the method falls short in capturing the complex, multidimensional risk-return dynamics of innovative financial assets like cryptocurrencies. To address this gap, this study introduces a novel, integrated hybrid Multi-Criteria Decision-Making (MCDM) framework that provides a structured, transparent, and robust approach to cryptocurrency fund selection. The framework seamlessly integrates three well-established operations research methodologies: the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP), and the Vlse Kriterijumsk Optimizacija I Kompromisno Resenje (VIKOR) algorithm. DEMATEL is utilized to map and analyze the intricate causal interdependencies among a comprehensive set of evaluation criteria, categorizing them into foundational “cause” factors and resultant “effect” factors. This causal structure informs the ANP model, which computes precise criterion weights while accounting for complex feedback and dependency relationships. Subsequently, the VIKOR algorithm is invoked to use these weights to rank cryptocurrency fund alternatives, delivering a compromise between optimizing group utility and minimizing individual regret. To illustrate the application and efficacy of the proposed method, a diverse set of 20 cryptocurrency funds is analyzed. From the analysis, it is shown that foundational criteria, such as “Fee (%)” and “Annualized Standard Deviation,” are the primary causal drivers of financial performance outcomes of funds. This proposed framework supports strategic capital allocation in a rapidly evolving domains of digital finance. Full article

Taxation on cryptocurrency is becoming critical in global fiscal governance as digital assets adapt to the modern reality of existing outside of traditional regulatory constructs. Theoretical and practical understanding of cryptocurrency taxation is quite new, and so a systematic review was designed to present the most recent empirical research evidence on the legal, fiscal and behavioral aspects of cryptocurrency taxation from across the globe. Using the PRISMA-2020 guidelines, a structured search was applied to the Scopus database on 21 May 2025, with the search terms “crypto-currency”, “cryptoasset” and “taxation.” The inclusion criteria consisted of original research articles published between the years of 2020 and 2025 in English or Spanish, that could be accessed via institutional library support, and that were related to taxation, legal regulation and/or compliance. Out of the original identified 224 records, 36 met the eligibility criteria after screening and verification through seven different stages of review. Socially, five themes were produced by the findings: legal ambiguity surrounding fiscal treatment, limited tax literacy and compliance issues, macroeconomic and monetary issues, application of digital technologies for fiscal tracking, and environmental repercussions from crypto mining. Many countries do not have any coherent tax frameworks to govern the risk that emerges from cryptocurrency taxation, creating uncertainty for both regulators and investors. The findings outlined in this systematic review point to the urgent need for creating a coherent approach to cryptocurrency taxation based on definitions, digital approaches to traceability, and tax literacy compliance strategies. In order to create effective cryptocurrency taxation, there must be a base balance between ensuring innovation, fiscal responsibility, transparency, equity and sustainability in the developing digital economy. Full article

This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin under extreme conditions, while linkages with U.S. Treasury bonds are weaker and often inverse. Net connectedness analysis reveals that the S&P 500 and Bitcoin act as the primary transmitters of shocks into FinTech indices, whereas Treasuries generally serve as receivers, except during stress episodes when safe-haven flows or heightened credit risk reverse the direction of spillovers. The dynamic ∆TCI (Difference between the total direct connectedness and the reverse total connectedness) further demonstrates that FinTech indices serve as net transmitters in stable markets but become receivers during crises such as the COVID-19 pandemic, the Federal Reserve’s tightening cycle of 2022–2023, and the FTX-driven crypto collapse. Segmental heterogeneity is also evident: distributed ledger firms are highly sensitive to cryptocurrency dynamics, alternative finance providers respond strongly to both equity and bond markets, and digital payments firms are primarily influenced by equity spillovers. Overall, the findings underscore FinTech’s dual role—transmitting shocks during tranquil periods but amplifying systemic vulnerabilities during crises. For investors, diversification benefits are state-dependent and largely disappear under adverse conditions. For regulators and policymakers, the results highlight the systemic importance of FinTech–equity and crypto–ledger linkages and the need to integrate FinTech exposures into macroprudential surveillance to contain volatility spillovers and safeguard financial stability. Full article

Since its launch in 2009, Bitcoin has become a market disruptor due to its primary function as a virtual currency supported by blockchain technology and the high volume of economic transactions it facilitates. This article examines the key theoretical principles that have contributed to Bitcoin’s recognition as a cryptocurrency. It assesses whether Bitcoin meets the criteria for being considered a form of money and evaluates its importance as a financial asset. This analysis of Bitcoin from 2014 to 2025 reveals that it does not sufficiently fulfill all the typical functions of money, such as serving as an internationally accepted means of payment, a unit of account, a securities depository, and a standard for deferred payments. Despite its usual close correlation with stock indices in financial markets, a decentralized digital currency like this still does not meet the requirements of fundamental analysis. In practice, this leads to its exclusion as a currency, since it does not fulfill the functions of money nor fully qualify as a crypto asset, as its value is primarily based on investors’ expectations of high returns. Apart from a lack of foundation in tangible goods or services that justifies their value and dependence on new investors, the findings do not indicate conditions typical of a developed pyramidal model. Nevertheless, this does not prevent future technological innovations from responding positively to the functions of money or from offering real money services, especially those related to service innovation and the digital economy. Full article

Accurate and efficient cryptocurrency price prediction is vital for investors in the volatile crypto market. This study comprehensively evaluates nine models—including baseline, zero-shot, and deep learning architectures—on 21 major cryptocurrencies using daily and hourly data. Our multi-dimensional evaluation assesses models based on prediction accuracy (MAE, RMSE, MAPE), speed, statistical significance (Diebold–Mariano test), and economic value (Sharpe Ratio). Our research found that the optimally fine-tuned TimeGPT model (without variables) demonstrated superior performance across both Daily and Hourly datasets, with its statistical leadership confirmed by the Diebold–Mariano test. Fine-tuned Chronos excelled in daily predictions, while TFT was a close second to TimeGPT for hourly forecasts. Crucially, zero-shot models like TimeGPT and Chronos were tens of times faster than traditional deep learning models, offering high accuracy with superior computational efficiency. A key finding from our economic analysis is that a model’s effectiveness is highly dependent on market characteristics. For instance, TimeGPT with variables showed exceptional profitability in the volatile ETH market, whereas the zero-shot Chronos model was the top performer for the cyclical BTC market. This also highlights that variables have asset-specific effects with TimeGPT: improving predictions for ICP, LTC, OP, and DOT, but hindering UNI, ATOM, BCH, and ARB. Recognizing that prior research has overemphasized prediction accuracy, this study provides a more holistic and practical standard for model evaluation by integrating speed, statistical significance, and economic value. Our findings collectively underscore TimeGPT’s immense potential as a leading solution for cryptocurrency forecasting, offering a top-tier balance of accuracy and efficiency. This multi-dimensional approach provides critical, theoretical, and practical guidance for investment decisions and risk management, proving especially valuable in real-time trading scenarios. Full article

The FTX collapse marked a significant shock to global crypto markets, prompting concerns about systemic contagion. This paper investigates the dynamic connectedness between cryptocurrencies, DeFi tokens, and tech stocks, focusing on the systemic impact of the FTX collapse. We decompose total, internal, and external connectedness across asset groups using a time-varying parameter VAR model. The results show that post-FTX, Bitcoin and Ethereum intensified their roles as core shock transmitters, while Tether consistently acted as a volatility absorber. DeFi tokens exhibited heightened intra-group spillovers and occasional external influence, reflecting structural fragility. Tech stocks remained largely insulated, with reduced cross-market linkages. Network visualizations confirm a post-crisis fragmentation, characterized by denser internal crypto-DeFi ties and weaker inter-group contagion. These findings have important policy implications for regulators, investors, and system designers, indicating the need for targeted risk monitoring and governance within decentralized finance. Full article

This paper examines the interconnection and wavelet coherence between the green cryptocurrency market and the green conventional market, utilizing daily data. The research period covers 1 July 2020 to 30 September 2024. Employing the time-varying parametric vector autoregression (TVP-VAR) model and wavelet coherence analysis, we capture both short- and long-term spillovers across markets. The results show that cryptocurrencies, particularly Binance and Litecoin, act as dominant transmitters of volatility and return shocks, while green conventional indices function mainly as receivers with strong self-dependence. Spillover intensity is highly time-varying, with peaks during periods of systemic stress, particularly during the COVID-19 pandemic, and troughs indicating diversification opportunities. These findings advance the literature on systemic risk and portfolio design by showing that crypto assets can simultaneously amplify vulnerabilities and enhance diversification when combined with green finance instruments. For policy, the results highlight the need for regulatory frameworks that integrate sustainability taxonomies, mandate environmental disclosures for digital assets, and incentivize energy-efficient blockchain adoption to align crypto markets with sustainable finance objectives. This research enhances our understanding of the interrelationship between green investments and cryptocurrencies, providing valuable insights for investors and policymakers on risk management and diversification strategies in an increasingly sustainable financial landscape. Full article

The recent economic turmoil and the increasing volatility of bitcoins have necessitated the need for exploring safe-haven assets for bitcoins. In this quest, the present study aims to investigate the safe haven for bitcoins by examining the dynamic relationship between bitcoins, gold, foreign exchange, and stablecoins. This is achieved by calculating hedge ratios and portfolio weight ratios for various asset classes, by employing adaptive-based techniques such as generalized orthogonal generalized autoregressive conditional heteroscedasticity, corrected dynamic conditional correlation, corrected asymmetric dynamic conditional correlation, and asymmetric dynamic conditional correlation under various market and time-varying conditions. The empirical estimate reveals that all the selected asset classes are effective risk diversifiers for bitcoins. However, among all the asset classes, as per the hedge and portfolio weight ratio, Japanese yen, stablecoin for Japanese yen and Great Britain Pound, and Crypto Holding Frank Token (lowest-cost hedging strategies) are the most effective risk diversifiers when compared with bitcoins. Moreover, while considering external economic shocks, the empirical estimate posits that stablecoins are more stable risk diversifiers compared to the asset class they represent. Furthermore, in terms of the bivariate portfolio analysis formed with bitcoin, this study concludes that the weight of bitcoin is more stable when combined with gold, tether gold, Euro, Great Britain Pound, Swiss franc, and Japanese Yen. Thus, these assets are attractive for long-term investment strategies. This study provides investors and policymakers with significant insight into understanding safe-haven assets for bitcoin’s volatility and constructing a flexible portfolio that is dependent on the investment timeline and the prevailing market conditions. Full article

This paper investigates systemic tail dependence within the crypto-asset ecosystem by examining interconnectedness across eight major tokens spanning Layer 1 cryptocurrencies, DeFi tokens, stablecoins, and infrastructure/governance assets. We employ a novel partial correlation-based network framework and quantile-specific connectedness measures to examine how co-movement patterns evolve under normal and extreme market conditions from September 2021 to March 2025. Unlike conventional correlation or variance decomposition approaches, our methodology isolates direct, tail-specific transmission channels while filtering out standard shocks. The results indicate strong asymmetries in dependence structures. Systemic risk intensifies during adverse tail events, particularly around episodes such as the Terra/Luna crash, the USDC depeg, and Bitcoin’s 2024 halving cycle. Our analysis shows that ETH, LINK, and UNI are key assets in spreading losses when the market falls. In contrast, the stablecoin DAI tends to absorb some of the stress, helping reduce risk during downturns. These results indicate critical contagion pathways and suggest that regulation targeting protocol-level transparency, liquidity provisioning, and interoperability standards may reduce amplification mechanisms without eliminating interdependence. Our findings contribute to the emerging literature on crypto-systemic risk and offer actionable insights for regulators, DeFi protocol architects, and institutional investors. In particular, we advocate for the incorporation of tail-sensitive network diagnostics into real-time monitoring frameworks to better manage asymmetric spillover risks in decentralized financial systems. Full article

Cryptocurrency markets are characterized by high volatility, nonlinear dependencies, and limited transparency, making short-term forecasting particularly challenging for both researchers and practitioners. To address these complexities, this study introduces a Temporal Fusion Transformer (TFT)-based forecasting framework that integrates on-chain and technical indicators to improve predictive performance and inform tactical trading decisions. By combining multi-source features—such as Spent Output Profit Ratio (SOPR), Total Value Locked (TVL), active addresses (AA), exchange net flow (ENF), Realized Cap HODL Waves, and the Crypto Fear and Greed Index—with classical signals like Relative Strength Index (RSI) and moving average convergence divergence (MACD), the model captures behavioral patterns, investor sentiment, and price dynamics in a unified structure. Five major cryptocurrencies—BTC, ETH, USDT, XRP, and BNB—serve as the empirical basis for evaluation. The proposed TFT model is benchmarked against LSTM, GRU, SVR, and XGBoost using standard regression metrics to assess forecasting accuracy. Beyond prediction, a signal-based trading strategy is developed by translating model outputs into daily buy, hold, or sell signals, with performance assessed through a comprehensive set of financial metrics. The results suggest that integrating attention-based deep learning with domain-informed indicators provides an effective and interpretable approach for multi-asset cryptocurrency forecasting and real-time portfolio strategy optimization. Full article

This study explores the characteristics of individual investors in crypto asset markets using machine learning and explainable artificial intelligence (XAI) methods. The primary objective was to identify the most effective model for predicting the likelihood of an individual investing in crypto assets in the future based on demographic, behavioral, and financial factors. Data were collected through an online questionnaire distributed via social media and personal networks, yielding a limited but informative sample. Among the tested models, Efficient Linear SVM and Kernel Naïve Bayes emerged as the most optimal, balancing accuracy and interpretability. XAI techniques, including SHAP and Partial Dependence Plots, revealed that crypto understanding, perceived crypto risks, and perceived crypto benefits were the most influential factors. For individuals with a high likelihood of investing, these factors had a strong positive impact, while they negatively influenced those with a low likelihood. However, for those with a moderate investment likelihood, the effects were mixed, highlighting the transitional nature of this group. The study’s findings provide actionable insights for financial institutions to refine their strategies and improve investor engagement. Furthermore, it underscores the importance of interpretable machine learning in financial behavior analysis and highlights key factors shaping engagement in the evolving crypto market. Full article

The Markets in Crypto-Assets Regulation (MiCA) is a comprehensive European Union regulatory framework aimed at harmonizing the crypto-asset market. The existing literature has mainly examined MiCA from a legal perspective, while empirical assessments of industry perspectives remain scarce. In this study, we examine MiCA’s impact on the crypto market and its implications for both theory and practice by analyzing and integrating insights from 12 expert interviews. The findings reveal perceived benefits arising from the unified market, enhanced investor protection, and compliance clarity, alongside challenges related to the high regulatory burden, legal ambiguities, and limited innovation support. On this basis, we provide recommendations for improving the regulatory framework and its implementation. Furthermore, we integrate our findings within the technology–organization–environment (TOE) framework to provide a theory-based starting point for rigorous academic research. These findings contribute to regulatory discourse and offer practical guidance for the relevant stakeholders, including businesses, regulators, policymakers, and academics. Full article

Our study examines the regulatory challenges and opportunities of asset tokenization within the context of the European Union (EU), emphasizing the balance between technological innovation and investor protection in the digital economy. Focusing on 2023 EU Markets in Crypto-Assets Regulation and its application in Latvia, we utilize comparative legal and integrative literature review methodologies to explore how regulatory frameworks can enhance investor accessibility, liquidity, and transparency in digital transactions. Our findings emphasize the importance of strong legal frameworks in promoting economic growth and protecting investors, thereby contributing to a more inclusive financial ecosystem. By examining the regulatory landscape for distributed ledger technology, we provide insights into how regulations can balance innovation in asset management with the imperative of investor protection. We offer a broad analysis of the intersection between legal frameworks and technological advancements in Latvia, illustrating how diverse regulatory approaches can support both economic development and investor interests. Our research originality lies in its focus on the EU’s regulatory diversity, particularly in Latvia, and its implications for broader European and international regulatory environments. Our study contributes to ongoing discussions on optimizing regulatory strategies to facilitate secure and advantageous financial technologies, reflecting the diversity of legal and economic approaches across Europe. Full article

Stablecoins are crypto assets designed to maintain stable value by bridging fiat currencies and volatile crypto assets. Our study extends previous research by analyzing the instability and co-movement of major stablecoins (USDT, USDC, DAI, and TUSD) during significant economic events such as the COVID-19 pandemic and the collapses of Iron Finance, Terra-Luna, FTX, and Silicon Valley Bank (SVB). We investigated the temporal volatility and dynamic connections between stablecoins using wavelet techniques. Our results showed that the announcement of USDT’s listing on Coinbase in April 2021 significantly impacted the stability of stablecoins, evidenced by a decline in the power spectrum. This phenomenon has not been explored in the literature. Furthermore, the collapse of SVB was highly relevant to the stablecoin market. We observed high coherence between pairs during the pandemic, the Coinbase listing, and the collapse of SVB. After the collapse of Terra-Luna, USDT, USDC, and DAI became more connected in the medium term, with USDC and DAI extending in the long term despite a negative co-movement between USDT and the others. This study highlights the impact of exchange listings on the volatility of stablecoins, with implications for investors, regulators, and the cryptocurrency community, especially regarding the stability and safe integration of these assets into the financial system. Full article

According to the latest data from CryptoSlam, as of November 2024, NFT sales have approached USD 7.43 billion, with trading profits exceeding USD 33.303 million. In the buyer–seller market, the potential demand for NFT transactions continues to grow, leading to rapid development in the NFT market and giving rise to various issues, such as price manipulation, counterfeit products, hacking of investment platforms, identity verification errors, data leaks, and wallet security failures, all of which have caused significant financial losses for investors. Currently, the NFT investment market faces challenges such as legal uncertainty, information security, and high price volatility due to speculation. This study conducted expert interviews and adopted a two-stage research methodology to analyze the most common risk factors when selecting NFT investments. It employed the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP) to explore risk factors such as legal issues, security concerns, speculation, and price volatility, aiming to understand how these factors influence investors in choosing the most suitable NFT investment platform. The survey was conducted between February and June 2023, targeting professionals and scholars with over 10 years of experience in the financial market or financial research, with a total of 13 participants. The empirical results revealed that speculation had the greatest impact compared to legal issues, security concerns, and NFT price volatility. Speculation and price volatility directly influenced other risk factors, potentially increasing the risks faced by NFT investment platforms. In contrast, legal and security issues had less influence on other factors and were more affected by them, indicating a relatively lower likelihood of occurrence. Thus, investors must be cautious of short-term speculation, particularly when dealing with rare NFTs. The best approach is to set an exit price to minimize potential losses if the investment does not proceed as planned. Full article