Search Results (661)

This study employs a bibliometric analysis of emerging trends in money laundering for terrorism financing (ML/TF) to provide direction for future research. The authors used VOSviewer and analyzed 2577 published documents retrieved from the SCOPUS database using the PRISMA methodology. The findings reveal a growing research interest in understanding the complex interplay between money laundering and terrorism financing. This research emphasizes the significance of ML/TF for economic stability, as understanding terrorism financing mechanisms allows authorities to trace and block funds going to terrorist groups, which is crucial for national security. Critical insights for policymakers underscore the need for robust legislative frameworks, effective Financial Intelligence Units (FIUs), and international collaboration to combat these global threats. This analysis offers a foundation for future research, mapping the evolving knowledge landscape in ML/TF. Full article

Pairs trading is a short-term speculation trading strategy based on matching a long position with a short position in two assets in the hope that their prices will return to their historical equilibrium. In this paper, we focus on identifying opportunities where mean reversion will happen quickly, as the commission costs associated with keeping the positions open for an extended period of time can eliminate excess returns. To this end, we propose the use of the local Hurst exponent as a signal to open trades in the cryptocurrencies market. We conduct a natural experiment to show that the spread of pairs with anti-persistent values of Hurst revert to their mean significantly faster. Next, we verify that this effect is universal across pairs with different levels of co-movement. Finally, we back-test several pairs trading strategies that include $H<0.5$ as an indicator and check that all of them result in profits. Hence, we conclude that the Hurst exponent represents a meaningful indicator to detect pairs trading opportunities in the cryptocurrencies market. Full article

Despite advances in biofuel production and biomass processing technologies, biorefineries still experience commercialization issues. When costs exceed revenues, their long-term economic sustainability is threatened. Although integrated biorefineries have significant global potential due to process integration and product co-generation, it is crucial that they generate a positive net return, thereby incentivizing their continual operation. Nonetheless, research and development into new system designs and process integration are required to address current biorefinery inefficiencies. The integration of Bitcoin mining into biorefineries represents an innovative approach to diversify revenue streams and potentially offset costs, ensuring the economic viability and commercial success of biorefineries. When using bio-H₂, a total of 3904 sats/kg fuel can be obtained as opposed to 537 sats/kg fuel when using syngas. Bitcoin, whether produced onsite or not, is an accretive asset that can offset the sales price of other produced biochemicals and biomaterials, thereby making biorefineries more competitive at offering their products. Collaborations with policy makers and industry stakeholders will be essential to address regulatory challenges and develop supportive frameworks for widespread implementation. Over time, the integration of Bitcoin mining in biorefineries could transform the financial dynamics of the bio-based products market, making them more affordable and accessible whilst pushing towards sustainable development and energy transition. Full article

This study examines the impact of market volatility and cryptocurrency holdings on corporate liquidity, with a particular focus on the differences between cryptocurrency exchanges and other businesses. The analysis is based on 181 firm-year observations from 2017 to 2022, using Bitcoin volatility, VIX, and VKOSPI as indicators of market volatility. Ordinary Least Squares (OLS) and robust regression analyses are employed to assess the relationships between these variables. It is first noted that, albeit insignificant, market volatility has a detrimental influence on company liquidity. The positive correlation for cryptocurrency exchanges, however, suggests that cryptocurrency exchanges could potentially leverage market volatility as a strategic advantage. Additionally, the study shows that cryptocurrency holdings enhance corporate liquidity, with a stronger association observed in cryptocurrency exchanges. The analysis also incorporates lagged variables to capture delayed effects, confirming that cryptocurrency holdings exert both immediate and delayed positive impacts on liquidity, likely due to effective strategic management practices within exchanges. Full article

Passwords are the first line of defence against preventing unauthorised access to systems and potential leakage of sensitive data. However, the traditional reliance on username and password combinations is not enough protection and has prompted the implementation of technologies such as two-factor authentication (2FA). While 2FA enhances security by adding a layer of verification, these techniques are not impervious to threats. Even with the implementation of 2FA, the relentless efforts of cybercriminals present formidable obstacles in securing digital spaces. The objective of this work is to implement blockchain technology as a form of 2FA. The findings of this work suggest that blockchain-based 2FA methods could strengthen digital security compared to conventional 2FA methods. Full article

Using daily returns of Bitcoin, Litecoin, Ripple and Stellar, we introduce a novel risk measure for quantitative-risk management in the cryptomarket that accounts for the significant co-movements between cryptocurrencies. We find that our model has a lower error margin when forecasting the extent of future losses than traditional risk measures, such as Value-at-Risk and Expected Shortfall. Most notably, we observe this in Litecoin’s results, where Expected Shortfall, on average, overestimates the potential fall in the price of Litecoin by 8.61% and underestimates it by 3.92% more than our model. This research shows that traditional risk measures, while not necessarily inappropriate, are imperfect and incomplete representations of risk when it comes to the cryptomarket. Our model provides a suitable alternative for risk managers, who prioritize lower error margins over failure rates, and highlights the value in exploring how risk measures that incorporate the unique characteristics of cryptocurrencies can be used to supplement and complement traditional risk measures. Full article

Digitalization has become more and more important for economic activities and economic development. E-commerce, as a part of local and international trade, is of increasing importance and is highly correlated with technological progress and innovation. Our research investigates the relevance of selected drivers that could explain the latest e-commerce adoption at global level. In our analysis, we used the UNCTAD B2C E-commerce Index (2014–2020, yearly data, covering 133 countries, 931 observations) which can be considered relevant to express an economy’s readiness to support e-commerce. E-commerce adoption is assessed in our research by the following six factors: (i) wealth, economic freedom, and economic development; (ii) access and sophistication of the financial sector; (iii) education; (iv) regulations; (v) development of ICT infrastructure; and (vi) frontier drivers (such as AI, cryptocurrencies, and blockchain technologies). In our research, we used a panel data analysis framework using hard data provided by different databases (UNCTAD, World Bank, etc.). The results we obtained confirmed that developed countries with a higher income level are higher adopters of e-commerce, financial development and the accessibility of financial services significantly help e-commerce adoption, a regulatory system (particularly economic freedom and property rights) strongly supports e-commerce adoption, education has a positive impact on e-commerce adoption, ICT infrastructure increases the adoption of e-commerce and the readiness and use of AI, and frontier technologies generate an increased adoption of e-commerce. The results we obtained are consistent with the findings of similar studies (most of them using different research methodologies) and opens the ground for interesting discussions and further research developments. The novelty of our research consists in the exhaustive perspective on e-commerce adoption drivers (including frontier technologies such as AI or blockchain) based on hard country data collected from various sources for a consistent panel of countries and a relevant number of years, providing an alternative approach to the mainstream studies on e-commerce adoption that process data from surveys, interviews, or focus groups. Full article

In recent years, the concept of the prosumer has garnered extensive attention across various fields, including marketing, energy consumption, and innovation research. This attention is driven by the significant role prosumers play in developing more efficient, sustainable, and health-conscious market systems, propelled by advancements in social and technological domains. Broadly defined, a prosumer is an individual who acts as both a producer and a consumer. Originally coined by Toffler in the 1980s, the term describes individuals who blur the lines between producers and consumers by engaging in the creation of value for their own use or for others. Prosumers are seen as external partners who participate in co-creation processes with organizations, contributing to innovative outcomes and the production of the products and services they consume. The concept of the prosumer, individuals who simultaneously act as producers and consumers, has gained significant attention across various sectors. This entry explores the evolving role and impact of prosumers on sustainability, innovation, and market dynamics. A comprehensive literature review and empirical analysis were conducted to understand prosumer behavior and contributions. The findings reveal that the prosumers’ roles range from significantly enhancing sustainability by generating renewable energy to promoting personalized education via teacher–prosumer models. They also drive technological advancements in fields such as 3D printing and cryptocurrency. The study concludes that prosumers have the potential to foster a more resilient and inclusive economy, although challenges such as regulatory barriers and technological dependencies must be addressed to fully leverage their contributions. Full article

With applications in computer networks, robotics, genetics, data center network optimization, cryptocurrency exchange, transportation and logistics, cloud computing, and social network analysis, the problem of sorting permutations on transposition trees under various operations is highly relevant. The goal of the problem is to sort or rearrange the markers in a predetermined order by swapping them out at the vertices of a tree in the fewest possible swaps. Only certain classes of transposition trees, like path, star, and broom, have computationally efficient algorithms for sorting permutations. In this paper, we examine the so-called $n-broom$ transposition trees. A single broom or simply a $broom$ is a spanning tree formed by joining the center of the star graph with one end of the path graph. A generalized version of a broom known as an $n-broom$ is created by joining the ends of n brooms to one vertex, known as the $n-broom$ center. By using the idea of clear path markers, we present a novel algorithm for sorting permutations on an $n-broom$ for $n>2$ that reduces to a novel $2-broom$ algorithm and that further reduces to two instances of a $1-broom$ algorithm. Our single-broom algorithm is similar to that of Kawahara et al.; however, our proof of optimality for the same is simpler. Full article

Blockchain technology is now an advanced and innovative database technology and the attributes of blockchains are apparent in a variety of industries, especially in the financial industry. One of the most famous blockchain use cases, cryptocurrencies, has provoked much interest in social network users and customers. According to CoinMarketCap’s information, the global crypto market capitalization has reached around USD 2.37 T and there are around 9975 different cryptocurrencies available in the market. Despite the fact that academia and industry have paid much attention towards the blockchain direction, there is not much research on the factors that influence customer acceptability. This paper studies blockchains from a different angle, probing the factors prompting customers to use financial applications that utilize blockchain technology. We established the model and sorted the individual factors of perceived information security, technology awareness and privacy and found that users’ acceptance is significantly affected by information security and technology awareness, while privacy does not significantly influence users. According to the findings, we provide useful insights for application developers, conclude by presenting the limitations of the research and provide guidelines for future research. Full article

Accurate risk assessment is crucial for predicting potential financial losses. This paper introduces an innovative approach by employing expected risk models that utilize risk samples to capture comprehensive risk characteristics. The innovation lies in the integration of classical credibility theory with expected risk models, enhancing their stability and precision. In this study, two distinct expected risk models were developed, referred to as Model Type I and Model Type II. The Type I model involves independent and identically distributed random samples, while the Type II model incorporates time-varying stochastic processes, including heteroscedastic models like GARCH(p,q). However, these models often exhibit high variability and instability, which can undermine their effectiveness. To mitigate these issues, we applied classical credibility theory, resulting in credible expected risk models. These enhanced models aim to improve the accuracy of Value-at-Risk (VaR) forecasts, a key risk measure defined as the maximum potential loss over a specified period at a given confidence level. The credible expected risk models, referred to as CreVaR, provide more stable and precise VaR forecasts by incorporating credibility adjustments. The effectiveness of these models is evaluated through two complementary approaches: coverage probability, which assesses the accuracy of risk predictions; and scoring functions, which offer a more nuanced evaluation of prediction accuracy by comparing predicted risks with actual observed outcomes. Scoring functions are essential in further assessing the reliability of CreVaR forecasts by quantifying how closely the forecasts align with the actual data, thereby providing a more comprehensive measure of predictive performance. Our findings demonstrate that the CreVaR risk measure delivers more reliable and stable risk forecasts compared to conventional methods. This research contributes to quantitative risk management by offering a robust approach to financial risk prediction, thereby supporting better decision making for companies and financial institutions. Full article

In recent years, cryptocurrencies have received substantial attention from investors, researchers and the media due to their volatile behaviour and potential for high returns. This interest has led to an expanding body of research aimed at predicting cryptocurrency prices, which are notably influenced by a wide array of technical, sentimental, and legal factors. This paper reviews scholarly content from 2014 to 2024, employing a systematic approach to explore advanced quantitative methods for cryptocurrency price prediction. It encompasses a broad spectrum of predictive models, from early statistical analyses to sophisticated machine and deep learning algorithms. Notably, this review identifies and discusses the integration of emerging technologies such as Transformers and hybrid deep learning models, which offer new avenues for enhancing prediction accuracy and practical applicability in real-world scenarios. By thoroughly investigating various methodologies and parameters influencing cryptocurrency price predictions, including market sentiment, technical indicators, and blockchain features, this review highlights the field’s complexity and rapid evolution. The analysis identifies significant research gaps and under-explored areas, providing a foundational guideline for future studies. These guidelines aim to connect theoretical advancements with practical, profit-driven applications in cryptocurrency trading, ensuring that future research is both innovative and applicable. Full article

This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market’s continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market. Full article

Bitcoin is a payment system that generates a decentralized digital currency without ensuring temporal constraints in its transactions; therefore, it is vulnerable to double-spending attacks. Karame has proposed a formalization for a successful double-spending attack based on meeting three requirements. This focuses on fast payment scenarios where the product is delivered immediately after the payment is announced in the mempool, without waiting for transaction confirmation. This scenario is key in Bitcoin to increase the probability of a successful double-spending attack. Different approaches have been proposed to mitigate these attacks by addressing one or more of Karame’s three requirements. These include the following: flooding every transaction without restrictions, introducing listeners/observers, avoiding isolation by blocking incoming connections, penalizing malicious users by revealing their identity, and using machine learning and bio-inspired techniques. However, to our knowledge, no proposal deterministically avoids double-spending attacks in fast payment scenarios. In this paper, we introduce DiFastBit: a distributed transaction differentiation scheme that shields Bitcoin from double-spending attacks in fast payment scenarios. To achieve this, we modeled Bitcoin from a distributed perspective of events and processes, reformulated Karame’s requirements based on Lamport’s happened-before relation (HBR), and introduced a new theorem that consolidates the reformulated requirements and establishes the necessary conditions for a successful attack on fast Bitcoin payments. Finally, we introduce the specifications for DiFastBit, formally prove its correctness, and analyze DiFastBit’s confirmation time. Full article

In the last few years, blockchain has emerged as a cutting-edge technology whose main advantages are transparency, traceability, immutability, enhanced efficiency, and trust, thanks to its decentralized nature. Although many people still identify blockchain with cryptocurrencies and the financial sector, it has many prospective applications beyond digital currency that can serve as use cases for which traditional infrastructures have become obsolete. Governments have started exploring its potential application to public services provision, as confirmed by the increasing number of adoption initiatives, projects, and tests. As the current public administration is often perceived as slow, bureaucratic, lacking transparency, and failing to involve citizens in decision-making processes, blockchain can establish itself as a tool that enables a process of disintermediation, which can revolutionize the way in which public services are managed and provided. In this paper, we will provide a survey of the main application areas which are likely to benefit from blockchain implementation, together with examples of practical implementations carried out by both state and local governments. Later, we will discuss the main challenges that may prevent its widespread adoption, such as government expenditure, technological maturity, and lack of public awareness. Finally, we will wrap up by providing indications on future areas of research for blockchain-based technologies. Full article