Cryptocurrency Assets Research Articles

Constructing Optimal Portfolios Using the Single Index Model and Markowitz Model: A Study on Cryptocurrencies

This study analyzes the formation of optimal portfolios on cryptocurrency assets using the single index model and the Harry Markowitz model. This study covers 79 cryptocurrencies with the largest market capitalization during the period June 2023–June 2024. We calculate the optimal portfolio using the single index model and Markowitz, and evaluate its performance using the Sharpe Ratio. The results show that the Harry Markowitz model produces better portfolio performance compared to the single index model. The Markowitz portfolio produces a positive Sharpe ratio (1.8496), a portfolio return rate of 7.678%, and lower risk (0.0415). Conversely, the single index model portfolio shows a negative Sharpe ratio (-2.0971), indicating lower returns than risk-free assets. In addition, the Markowitz model offers more efficient diversification than the single index model. However, in general, both the Single Index Model and the Markowitz Model have a significant effect on the formation of optimal portfolios, with the Sharpe Index proving to be a significant mediator in the relationship between the two models and the optimal portfolio. The R-squared value shows that the SIM variables, Markowitz Model, and Sharpe Index explain 48.4% of the variation in the optimal portfolio. This study recommends the use of the Harry Markowitz model for cryptocurrency investment because it can provide higher returns with more controlled risks. This study provides important insights for investors on the strategy of diversifying cryptocurrency asset portfolios.

The Impact of Cryptocurrency Markets on the Traditional Financial Markets of the USA, UK, and Germany

The acceleration of the globalization process and the structural changes in technology that emerged in the 2000s have affected financial markets. This interaction in the financial markets has made the emergence of new financial assets necessary. According to the ARDL boundary test results, there is no significant relationship between cryptocurrency markets and stock returns in both the long and short term for the UK financial markets. For the German financial markets, it has been determined that there is a significant and positive long-term relationship between the cryptocurrency market assets Bitcoin and Tether and stock market returns. In the short term, no significant relationship has been detected. For the long term in the U.S. financial markets, it has been determined that there is a significant and positive relationship between Bitcoin, a cryptocurrency market asset, and stock market returns, while there is no significant relationship between Ethereum and Tether with stock market returns. In the short term, no significant relationship has been detected. These findings offer significant implications for policymakers, investors, and market analysts.

Investment Behavior and Strategy in Cryptocurrency in Indonesia

Investor behavior in making investment decisions is always based on clear reasons, whether for reasons of the future, profit, or hedging. This also provides a more precise portrait of investor behavior and the learning process if you want to enter the crypto asset industry. Apart from that, future suggestions for future researchers are expected to include female informants as a new perspective in researching investor behavior. The aim of carrying out this research is to find out what strategies can be implemented by cryptocurrency asset investors in order to get an optimal level of return. The method approach used in this research is qualitative library research. The data sources used as references in this research are data originating from articles, books, and newspapers from electronic media with the search keywords: how to invest in crypto, crypto investing techniques, and crypto investing strategies. The data obtained was then analyzed using NVivo 10 Plus software. From the results of this research, several strategies for investing in cryptocurrency assets were obtained, namely: determining the type and purpose of investment, studying the cryptocurrency assets you are interested in, choosing trusted cryptocurrency assets, choosing safe exchanges and wallets, paying attention to service fees and spreads, don't be afraid of missing out ( FOMO), buying when prices are low (support) and selling when prices are high (resistance), diversifying and mitigating risk, investing on various platforms, not easily panicking and oriented towards holding on for dear life (HODL), always following development information cryptocurrency, following and joining cryptocurrency communities, and regularly doing dollar-cost averaging (DCA).

Procedural Aspects of Cryptocurrency Arrest in National Legal Systems: A Comparative Analysis

The article presents an analysis of the procedures for arresting cryptocurrencies in various national legal systems, emphasizing the relevance of this topic in light of the rapid development of the digital economy and the increase in crimes related to cryptocurrencies. It draws attention to the absence of a unified international approach to regulating and controlling cryptocurrency assets, which creates challenges for law enforcement agencies in combating crime and money laundering. By analyzing the legislation of different countries, various approaches to defining the status of cryptocurrencies, mechanisms of their arrest, and their use as evidence in criminal proceedings have been identified. Special attention is given to the comparative analysis of the procedural aspects of cryptocurrency arrest, which allows for the identification of best practices and the development of recommendations for improving criminal procedural legislation. Solutions to the practical problems of jurisdiction and technical aspects of executing arrest decisions are proposed, emphasizing the importance of international cooperation in this field.

Cryptocurrency and financial crimes: A bibliometric analysis and future research agenda

The use of cryptocurrency for financial crimes has increased in recent years because of its decentralized and anonymous nature. This study extracted scholarly articles from the Scopus database and adopted bibliographic and content analysis to review financial fraud research in cryptocurrency. In addition, this study discussed the top ten cryptocurrency scams, potential reasons for falling into those traps, and associated theories to explore scammers’ behavior and outlined comprehensive future research guidelines for a safer financial world. Since 2018, the publication trend of revealing cryptocurrency frauds has gained momentum, and research on this topic has increased significantly in the last two years. The USA is the most significant contributor to cryptocurrency scam research. We found that both developed and developing countries are fairly concerned about combatting crypto fraudsters even though there are no regulated guidelines across the countries. The research potential has shifted from malware, bitcoin, and blockchain to fintech-based crimes such as money laundering, pump-and-dump schemes, and phishing. We observed that ICO fraud, money laundering, Ponzi schemes, phishing, darknet market transactions, ransomware, and pumps and dumps are some of the predominant crimes in crypto and that investor overconfidence, speculative expectations, low barriers to entry, decentralization, and anonymity are the primary reasons for crimes in cryptocurrency. This study suggests studying the socioeconomic impacts of cryptocurrencies, the necessity for standardized global regulation, and the integration of interdisciplinary research. Future research should emphasize exploring the innovation cycle in cryptocurrency assets, understanding cybercrime dynamics, guarding against crypto market manipulation, and developing automated scam prevention.

Optimizing Cryptocurrency Returns: A Quantitative Study on Factor-Based Investing

This study explores cryptocurrency investment strategies by adapting the robust framework of factor investing, traditionally applied in equity markets, to the distinctive landscape of cryptocurrency assets. It conducts an in-depth examination of 31 prominent cryptocurrencies from December 2017 to December 2023, employing the Fama–MacBeth regression method and portfolio regressions to assess the predictive capabilities of market, size, value, and momentum factors, adjusted for the unique characteristics of the cryptocurrency market. These characteristics include high volatility and continuous trading, which differ markedly from those of traditional financial markets. To address the challenges posed by the perpetual operation of cryptocurrency trading, this study introduces an innovative rebalancing strategy that involves weekly adjustments to accommodate the market’s constant fluctuations. Additionally, to mitigate issues like autocorrelation and heteroskedasticity in financial time series data, this research applies the Newey–West standard error approach, enhancing the robustness of regression analyses. The empirical results highlight the significant predictive power of momentum and value factors in forecasting cryptocurrency returns, underscoring the importance of tailoring conventional investment frameworks to the cryptocurrency context. This study not only investigates the applicability of factor investing in the rapidly evolving cryptocurrency market, but also enriches the financial literature by demonstrating the effectiveness of combining Fama–MacBeth cross-sectional analysis with portfolio regressions, supported by Newey–West standard errors, in mastering the complexities of digital asset investments.

Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency

AbstractThis paper proposes the use of social media as a proxy for financial information. Using an extended sample of 53,580,759 tweets and employing text analysis tools (Latent Dirichlet Allocation and Term Frequency–Inverse Document Frequency), we determine the information being exchanged on any given day. We train machine‐learning classifiers and forecast crypto price movements for more than 8000 cryptocurrencies and gauge market efficiency through successful forecasts based on public information. We propose various metrics of market efficiency for cryptocurrency assets and demonstrate that market efficiency is higher during the first 6 months after the Initial Coin Offering. We also examine the efficiency behavior of individual currencies during crisis periods.

Cryptocurrency-crime Investigation: Fraudulent use of Bitcoin in a Divorce Case

Bitcoin and cryptocurrency adoption has increased significantly over the past few years. The significant growth in the industry has been matched by growth of crimes in this domain; not only in scams and dark-web illegal trading, but also in white-collar crimes with fraud and perjury occurring increasingly. With blockchain technology, the world of financial infidelity has become increasingly sophisticated. There is a common belief that blockchain and cryptocurrency provide means of hiding funds from the public or close associates who may not be familiar with the technology. The rise of cryptocurrency has also led to spouses hiding digital assets during divorce settlements. This study presents a use case of a couple in the midst of a divorce where one of the spouses was accused of perjury for failure to declare bitcoin holdings, obtained via Bitcoin mining, and possibly other forms of cryptocurrency and digital assets to the court. The plaintiff is entitled to fifty percent of all assets. While property, stocks, bonds, and bank accounts can easily be traced, cryptocurrency assets are more complex to trace but it is not impossible. This paper illustrates how such a case can be investigated by following the flow of funds on the blockchain, using tools such as Maltego and QLUE. The paper thus presents an investigative process that can be followed for a new category of forensic investigation.

THE CRYPTOCURRENCY ASSETS MARKET IN THE CONDITIONS OF MILITARY AGGRESSION

The Russian-Ukrainian war transformed the needs of consumers of financial services, both in Ukraine and abroad. On the one hand, the civilian population and the Armed Forces of Ukraine needed immediate help from the international community. On the other hand, individual and institutional investors were concerned about threats to traditional financial investments. The purpose of the article is to identify the priority directions for the development of the cryptocurrency asset market against the background of the Russian-Ukrainian war. The article presents an analysis of the cryptocurrency asset market with an emphasis on its structural changes in the conditions of the Russian-Ukrainian war. In the study, a survey was conducted to find out the impact of the war on the Ukrainian cryptocurrency market. The analysis of the survey results substantiates the hypothesis about the impact of increased hacking attacks by pro-Russian forces and missile attacks on the functioning of the cryptocurrency market in Ukraine. It has been confirmed that investments and payments are promising segments of the cryptocurrency market in Ukraine, in which, the most interested users and potential users of cryptocurrency are. Based on the received data, the weaknesses and strengths of the functioning of the cryptocurrency market in the conditions of war were identified. Threats and opportunities for the development of the cryptocurrency market against the background of Russian armed aggression are substantiated. The conducted research made it possible to formulate scenarios for the development of the cryptocurrency asset market in Ukraine in war conditions and the conditions for their implementation.

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The Legitimacy of Tax Imposition on Bitcoin in the Commodity Futures Exchange

Bitcoin is ctyptocurrency assets traded on the physical market of cryptocurrency assets on commodity future exchanges. The purpose of this research is to determine and explore the legal status and regulations regarding taxation of bitcoin conducted on commodity future exchanges. This research is conducted of secondary using a normative juridical method, which involves the use and collection of secondary data and focuses on legal regulations. The result of the research indicate that the legal status of bitcoin taxation on commodity future exchanges has been regulated by applicable laws and is recognized based on legal theories and princples.

КРИПТОВАЛЮТЫ И СТЕЙБЛКОИНЫ: ПЕРСПЕКТИВЫ, ПРОБЛЕМЫ И ВОЗМОЖНОСТИ ДЛЯ ФИНАНСОВОЙ СИСТЕМЫ РОССИИ

The article analyzes the role of stablecoins and cryptocurrencies in the Russian financial system, their impact on investment markets, as well as the specific risks and opportu-nities associated with their use. The controversial issues of the essence of cryptocurrency are investigated. It has been revealed that in the scientific and practical environment cryptocurren-cies are considered as digital currencies, electronic means of payment, new forms of electronic money. Special attention is paid to the role of cryptocurrencies for the financial system, in par-ticular, its regulation, development prospects and impact on the country's financial market. The potential of stable coins to increase financial stability has been assessed. Key risks such as regulatory uncertainty, financial security and volatility of cryptocurrency assets have been iden-tified. The potential possibilities of using crypto assets to diversify investment portfolios in Russia are considered.

Dependence structure between NFT, DeFi and cryptocurrencies in turbulent times: An Archimax copula approach

This paper investigates the connectedness among eighteen cryptocurrency assets including NFT, DeFi, gold-backed cryptocurrencies, and traditional cryptocurrencies. We also compute the Optimal hedge ratio for each pair of (gold-backed) cryptocurrency-NFT/DeFi and assess their hedge effectiveness. To this end, we use a combination of econometric methods. Our sample period goes from 01/11/2021 to 21/02/2023, making the empirical analysis insightful and interesting as it includes the Covid-19 health crisis and the Russia–Ukraine war. Our empirical findings highlight the dissimilarities between different cryptocurrencies in terms of connectedness with NFT/DeFi assets. They also reflect the diversification benefits generated by the inclusion of gold-backed cryptocurrencies into NFT/DeFi portfolios, in particular in times of unprecedented events. These findings could be useful for crypto-investors who search to diversify their portfolios.

Informed trading and cryptocurrencies. New evidence using tick-by-tick data

Do trade size preferences exist in cryptocurrencies? Using high-frequency tick-by-tick data of cryptocurrencies, we examine what trade sizes contribute to daily cumulative price changes, viz. small, medium, and large. We employ robust estimations and document the presence of informed trading in the cryptocurrency asset class, and this is specifically translated via medium and small trades. We provide evidence to support the presence of stealth trading in cryptocurrencies. Our results contradict the prevailing view that informed trades in the cryptocurrency market occur via large trades.

Performance of the Realized-GARCH Model against Other GARCH Types in Predicting Cryptocurrency Volatility

Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has been particularly under-explored in the literature involving cryptocurrency volatility. This study emphasizes an investigation on the performance of the Realized-GARCH against a range of GARCH-based models to predict the volatility of five prominent cryptocurrency assets. Our analyses have been performed in both in-sample and out-of-sample cases. The results indicate that while distinct GARCH models can produce satisfactory in-sample fits, the Realized-GARCH model outperforms its counterparts in out of-sample forecasting. This paper contributes to the existing literature, since it better reveals the predictability performance of Realized-GARCH model when compared to other GARCH-types analyzed when an out-of-sample case is considered.

Do Asian Islamic Equities Offer Diversification Benefits in Cryptocurrency Portfolio in Times of Increased Uncertainty?

This study explores whether Islamic equities offer portfolio diversification benefits to cryptocurrency investors. It employs the Continuous Wavelet Transform model to examine the nature of coherence between major cryptocurrency asset classes and major Asian Islamic equity markets on different investment horizons. We consider a range of Islamic equity indices for multiple countries and a basket of three prominent cryptocurrencies: Bitcoin, Ethereum and Ripple. Findings suggest that Asian Islamic equities offer portfolio diversification opportunities. Our findings also imply that Asian Islamic equities are not efficient and are prone to short-term speculative activities.

Use of Patterned Datasets (Minimum and Maximum) to predict Bitcoin and Ethereum price movements

Bitcoin is always interesting to keep predicting where the next price movement will go. Bitcoin is the first and most influential Cryptocurrency on cryptocurrency price movements. Bitcoin is traded in many markets, the largest in Indonesia is Indodax. Indodax provides a document sharing API so that third parties can build applications that are able to process data on bitcoin price movements in real-time and continuously. This research shows how patterned datasets can be applied to monitor bitcoin price movements from the indodax market and show their effects on other cryptocurrency assets besides bitcoin. This research shows how data that is patterned and then processed using the minimum and maximum functions can provide 2 important information, namely the potential position of bitcoin when it is at the maximum and minimum points. The results of the patterned dataset formula are then compared to the movements of the 2 cryptocurrencies with the largest capitalization, namely BTC (Bitcoin) and ETH (Ethereum). The results of the comparison show that the use of patterned dataset formulas successfully shows important points in cryptocurrency trading, with simpler instructions.

Activities of internal affairs bodies in the process of detection and investigation of crimes committed with the use of information and communication technologies (on the example of cryptocurrency assets)

This article discusses topical issues of the activities of employees of the internal affairs bodies in the process of disclosing and investigating crimes committed with cryptocurrency assets. In the process of global digitalization, society has switched to the use of information and telecommunication technologies in various spheres of life. The trend of using information and telecommunication technologies is gaining momentum in the illegal strata of society. Criminals are increasingly using innovative technologies to commit crimes. The study provides a comprehensive analysis of the concept and features of cryptocurrency, as well as the most common crimes that infringe on cryptocurrency assets and (or) are committed with their use. The effectiveness of the activities of internal affairs bodies in the process of detecting and investigating crimes committed using information and communication technologies (on the example of cryptocurrency assets) is based on organizational forms of interaction between departments of internal affairs bodies. In conclusion, an algorithm of actions is proposed aimed at improving the efficiency of operational activities in the fight against the illegal use of information and communication technologies, in particular, cryptocurrency assets.

Economic sentiment and the cryptocurrency market in the post-COVID-19 era

This paper analyzes the interactions, dependence and dynamic network connectedness among economic based-news sentiment index (ESI) and the top eight crypto-currencies with particular attention on the post-COVID-19 pandemic era. We explore these features by applying novel econometric frameworks such as wavelets coherence, cross quantilogram, and DCC-GARCH-based connectedness techniques on a dataset that covers both the first and second waves of the COVID-19 outbreak. First, results from the wavelets approach show convincing evidence of low covariances between ESI and both returns and volume while results of correlations highlight the potential diversification opportunities of some crypto-currencies, especially during bullish market condition since the COVID-19 pandemic. Results from the directional predictability show that across all market conditions, there are strong evidence that ESI can predict the price return of stable-coins (Tether and USD) compared to the price return of Bitcoin, Ethereum and Dogicoin that ESI predicts only under bullish market condition. For changes in volume, we find that the predictive power of ESI is more pronounced and heterogeneous, cutting across different quantiles of the different classes of cryptocurrencies, especially Bitcoin (BTC) and Dogicoin (DOG). The analysis of dynamic connectedness show that the level of connectedness among ESI and both price return and changes in volume of trade of the chosen cryptocurrencies increased sharply during the peak of the COVID-19 pandemic. In both cases, Tether and ESI are the main destination of volatility shocks Ripple and Bitcoin are the dominant sources of shocks. We document the importance of economic sentiment for investors in the management of their cryptocurrency assets during periods of financial downturns as witnessed since the COVID-19 pandemic.

Decision Support System for Selecting the Best Cryptocurrency Mining Machine Using the Multifactor Evaluation Process Method

Cryptocurrency mining is a process carried out using a specialised computer network in order to obtain new crypto assets. The cryptocurrency mining business in today's digital era is increasingly in demand by netizens. Many netizens run cryptocurrency mining businesses to generate new cryptocurrency assets that can be traded on the cryptocurrency market to earn huge profits. In doing cryptocurrency mining itself, it is necessary to be careful in choosing the cryptocurrency mining machine used to get the maximum profit. In this study, researchers proposed the Multifactor Evaluation Process as a decision support system method used to simplify the process of selecting the best cryptocurrency mining machine. The results of this study show that the best cryptocurrency mining machine that is most recommended to use is Cheetah Miner F5I (0.2120), followed by the alternatives iBeLink DSM7T Miner (0.2072), Bitfury RD4 (0.2016), Aladdin T1 16T (0.2016), and Obelisk SC1 Dual (0.1800).