Cryptocurrency Market Research Articles

The role of stable coins in mitigating volatility in cryptocurrency markets

This study aims to analyze the link between Perceived Volatility Reduction (PVR), Risk Perception, Stablecoin Usage Frequency, Market Confidence, and Stablecoin Adoption (SA). The primary goal is to determine if and to what degree these variables impact stablecoin adoption. We created a questionnaire to gather information from 198 Malaysians. To analyze the research model and test the hypotheses, the Structural Equation Modeling-Partial Least Squares (SEM-PLS) method was utilized. According to the findings, there is a strong and positive association between Perceived Volatility Reduction (PVR) and Stablecoin Adoption (SA). Market players are more likely to adopt stablecoins if they perceive them as useful instruments for mitigating the severe price volatility inherent in traditional cryptocurrencies. This finding emphasizes the importance of risk perception and market stability in driving market behavior. Trust in stablecoin systems, transparency, and regulatory, compliance influenced PVR and SA. The study's findings underscore the significance of perceived volatility reduction (PVR) in driving stablecoin adoption (SA), highlighting the importance of risk perception and market stability. Trust in stablecoin systems, transparency, and regulatory compliance emerge as crucial factors influencing PVR and SA. These insights offer valuable guidance for investors navigating the cryptocurrency market, governments managing stablecoin supply, and scholars studying trust dynamics in the cryptocurrency ecosystem.

Exchange market share, market makers, and murky behavior: The impact of no-fee trading on cryptocurrency market quality

This study examines the impact of zero fees on market quality. This issue is examined using a natural experiment in Bitcoin provided by the Binance exchange, which eliminated maker–taker trading fees for market participants in July 2022. I find that although zero fees increase investors’ willingness to trade, thereby prima facie increasing liquidity, their elimination encourages market makers to widen the bid–ask spread and provide a shallower market depth, which in turn reduces liquidity. Liquidity providers realize gains at the expense of liquidity takers, suggesting the emergence of new potential forms of unethical financial market conduct. Notably, despite the removal of trading fees, total transaction costs increased for customers. These outcomes, coupled with the boost in exchange market share, raise concerns about price integrity and investors’ protection in the highly unregulated crypto environment, in turn implying that the elimination of maker–taker fees is harmful to the market.

Unravelling systemic risk commonality across cryptocurrency groups

This study explores the systemic risk within thirty-four diverse cryptocurrencies, analyzing the commonality across different groups. In light of the cryptocurrency market's significant downturn following the FTX collapse in 2022, this research uniquely examines systemic risk commonality. Interestingly, it reveals no distinct risk-reducing traits in sharia-compliant and gold-backed coins, suggesting asset backing does not mitigate inherent cryptocurrency risks. Moreover, a notable common trend in systemic risk among cryptocurrencies is identified, driven by their complementary characteristics. This insight into common systemic risk trends enables investors to make informed hedging decisions across various cryptocurrency groups, providing a safeguard against severe market downturns.

The effect of policy uncertainty on the volatility of bitcoin

PurposeThis paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022.Design/methodology/approachIn this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term.FindingsThe results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future.Originality/valueThis study has two implications. First, to the best of the authors’ knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment.

Mining in Russia. Cryptocurrencies as investment

In modern conditions, when parity international financial and monetary relations are turning from traditional instruments of economic interaction into instruments of neocolonial political pressure and blackmail of economically strong countries against weaker ones, the need for a new global financial order and monetary standard, that would be independent of the will of any central bank, but with a value as reliable as that of modern fiat currencies, is increasingly realized. Today, the global economy feels the urgent need for fundamentally new opportunities for market interaction and monetary exchange. Cryptocurrencies have become such tools — electronic decentralized money issued by specific individuals in specialized open computer networks through the transformation of electricity into fixed computer codes. The process of such transformation is called mining, which uses ASIC miners as computer equipment to obtain cryptocurrency. The concept of cryptocurrencies and the specifics of investing in them are considered. The characteristic is presented of the current state and dynamics of the development of the crypto market. The possibilities and methods of forecasting the development of the cryptocurrency market are shown both using classical methods of economic forecasting and a fundamentally new methodology based on the current assessment of the psychological state of participants in the crypto market and their readiness to commit certain economic actions. An assessment is given of the possibilities of investing in mining as a technology for mining cryptocurrencies. Requirements to special mining equipment — ASIC miners — are described. An assessment is given of the prospects for the development of the crypto economy.

Regulation of the cryptocurrency market by legislation of Hungary

The article is devoted to the study of the regulation of the cryptocurrency market in Hungary, the regulatory consolidation of property rights to cryptocurrency values. A comparative analysis of the regulation of this issue under the legislation of the EU and directly of Hungary was also carried out. Because it is the regulatory framework and court precedents on the territory of the EU that form the basis for the formation of the domestic framework for the regulation of Hungary’s crypto market. It is also important to clearly define Bitcoin and Blockchain as a special kind of thing in Hungarian civil law. The authors paid special attention to the innovation in the tax sphere of Hungary. Virtual currencies, especially cryptocurrencies, and the technology behind them and their constant development pose a serious challenge to the legislator. The task is further complicated by the fact that cryptocurrencies have a number of unique characteristics that distinguish them from one another. The study focuses on issues related to taxation, mainly based on the norms of the current legislation of Hungary. A study of the appearance, concept and social acceptance of money, a presentation and a detailed analysis of each financial function lead to an analysis into the world of cryptocurrencies. The article also argues that each of the payment documents in the form of cryptocurrency can act as digital currencies or cannot perform monetary functions. When writing the article, special attention was paid to digital money of the central bank, which can revolutionize it, as a digital form of legal tender. Nowadays, most countries (for example, England) are experimenting with central bank digital money for its implementation. In theory, central bank digital money can replace traditional money and payment services (eg bank accounts, bank deposits). The advantage of the system is that it would reduce the cost of bank funding and it is also new for monetary policy. Central bank regulation of digital money will be one of the most important issues in the coming years. Due to the extreme volatility of the field under consideration, the authors consider it important to note that this article can only provide a brief and concise overview of the regulatory and tax regulation of the cryptocurrency market under Hungarian law. And this analysis has a positive foundation for further research and comparison with the domestic legal base of cryptocurrency market regulation under the legislation of Ukraine.

Research on cryptocurrency portfolio based on Markowitz model

In recent years, the booming growth of the cryptocurrency market has garnered significant attention from a diverse range of investors with the overall market capitalization experiencing continuous expansion. This surge in popularity, however, is accompanied by challenges arising from the market's inherent high volatility and complexity. The increasing diversity of available assets presents investors with a complex landscape to navigate. The quest to build an optimal investment portfolio has become a focal point, particularly as market participants seek strategies that not only capitalize on the market's potential for substantial returns but also effectively manage the inherent risks. In this paper, nine cryptocurrencies have been chosen to construct the portfolios, which include BTC, Ether, Tether, Binance, ripple, USDC, ADA, DOGE and TRX. The data selected comprises the daily price of the above assets from November 1, 2020, to October 9, 2023. This paper presents a comprehensive framework for constructing and optimizing cryptocurrency investment portfolios, by employing the Markowitz model and Sharpe ratio analysis. We examined these nine selected digital currencies to determine the optimal investment allocation. Our findings suggest that the highest sharpe ratio is achieved when each investment cryptocurrencies (according to the order above) allocates 0, 0.003, 0.762, 0.038, 0.005, 0.172, 0, 0.014 and 0.007, respectively, to each cryptocurrency. And the lower risk portfolio is obtained when each cryptocurrencies accounted for 2.740e-20, 6.315e-20, 0.5, 0, 0.001, 0.499, 0, 1.419e-04 and 4.210e-20, respectively. This study contributes to the sustainable development of the cryptocurrency market by providing valuable insights for financial institutions, policymakers, and other stakeholders. However, limitations of this research come from the small number of selected digital currencies and the exclusion of risk-free assets such as government bonds. Future research should consider expanding the sample size and incorporating risk-free rates to enhance the applicability and reliability of investment strategies. In conclusion, this research offers investors a practical framework to navigate the complexities of the cryptocurrency market and optimize their investment decisions for long-term value appreciation.

Exploring Calendar Effects in Bitcoin Returns: An Analysis of Market Efficiency

Abstract This study delves into the exploration of calendar effects within Bitcoin returns to examine the validity of the Efficient Market Hypothesis (EMH) in the context of the cryptocurrency market. Leveraging data spanning from October 2015 to November 2021, this research employs regression analysis and power ratio analysis to investigate the presence of day-of-the-week and intraday effects on Bitcoin prices. The findings reveal statistically significant anomalies for Fridays and specific intraday periods, suggesting the potential for abnormal returns. However, these calendar effects are not pervasive enough to conclusively impact overall market efficiency. The study's results indicate that while Bitcoin's market may exhibit short-term inefficiencies, it largely conforms to the principles of market efficiency over extended periods. This research contributes to the ongoing discourse on the efficiency of cryptocurrency markets and highlights the necessity for further investigation using diverse methodologies to fully understand the dynamics at play. Keywords: Calendar Effect, Bitcoin Price, Regression Analysis, Power Ratio.

Cryptocurrency price fluctuation and time series analysis through candlestick pattern of bitcoin and ethereum using machine learning

PurposeCandlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.Design/methodology/approachThe data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.FindingsThe inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.Research limitations/implicationsTo statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).Originality/valueThis study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions.

The cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations. In this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly. To make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.

Beyond hype: Unveiling the herd effect in ESG and non-ESG cryptocurrency portfolios

This paper investigates herding behavior in the cryptocurrency market, specifically comparing environmental, social, and governance (ESG)-designated assets and non-ESG assets. It addresses a research gap concerning investor behavior towards ESG and non-ESG cryptocurrencies. Our study reveals that investor herding behavior manifests in specific years and in response to significant events, such as El Salvador’s adoption of Bitcoin. Notably, the findings suggest the presence of intentional herding behavior in 2023 data for ESG and non-ESG cryptocurrencies. These insights offer valuable guidance for investors and policymakers seeking to mitigate risks and optimize portfolio allocation strategies.

BITCOIN PRICE PREDICTION BY USING ARIMA

Cryptocurrency markets have emerged as a dynamic and intriguing domain, with Bitcoin at the forefront, captivating the attention of investors, researchers, and enthusiasts alike. The volatile nature of Bitcoin prices presents both opportunities and challenges for market participants seeking to understand and anticipate its movements. In this study, we delve into the realm of time series analysis to explore the feasibility of predicting The research journey begins with meticulous data preprocessing steps to ensure the quality and integrity of the input data. Leveraging Python libraries such as pandas and NumPy, we cleanse and format the historical Bitcoin price data, laying the foundation for subsequent analysis. Key preprocessing tasks include handling missing values, normalization, and addressing any anomalies or outliers that may distort the underlying patterns. With the data prepared, our attention turns to assessing the stationarity of the Bitcoin price time series—a fundamental prerequisite for applying classical time series models. Through visual inspection and statistical tests such as the Augmented Dickey-Fuller (ADF) test, we ascertain the presence of trends or seasonality that could influence the modelling process. To mitigate such effects, we employ techniques such as differencing and transformations, including the Box-Cox transformation, to stabilize the variance of the data. Armed with a stationary time series, we embark on the core of our analysis: modelling Bitcoin prices using Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) models. These models, renowned for their versatility and effectiveness in capturing temporal dependencies, offer a sophisticated framework for forecasting time series data. Guided by the principles of parsimony and model selection criteria such as the Akaike Information Criterion (AIC), we systematically explore the parameter space to identify the optimal specifications for our models. The efficacy of the chosen models is rigorously evaluated through diagnostic checks, encompassing residual analysis, model fit statistics, and out-of-sample validation. Insights gleaned from these assessments inform our confidence in the models' predictive capabilities and guide our interpretation of the forecasted outcomes. Finally, armed with a validated model, we turn our gaze to the future, employing it to generate forecasts of Bitcoin prices for forthcoming periods. Visualizations juxtaposing predicted prices against observed values provide a compelling narrative of the model's performance and offer stakeholders valuable insights into potential market trends and dynamics. In summary, this research contributes to the burgeoning field of cryptocurrency analytics by showcasing the application of time-tested statistical methodologies to forecast Bitcoin prices. By leveraging the power of ARIMA and SARIMAX models, we illuminate the intricate patterns underlying Bitcoin's price dynamics, empowering market participants with actionable intelligence for informed decision-making in an ever-evolving landscape. Keywords Cryptocurrency Markets, Bitcoin Price Prediction, Time Series Analysis, Python Programming, Data Preprocessing, Pandas, NumPy, Stationarity Testing, Augmented Dickey-Fuller (ADF) Test, Box-Cox Transformation, ARIMA Model, SARIMAX Model, Model Selection, Akaike Information Criterion (AIC), Diagnostic Checks, Residual Analysis, Out-of-Sample Validation, Forecasting, Visualization, Market Trends, Decision-Making, Cryptocurrency Analytics

Identification of awareness of the conditions for using cryptocurrencies in Poland

The primary aim of this article is to identify internet users' knowledge and awareness of cryptocurrencies and the determinants of their use in Poland. The study, which used the CAWI (Computer-Assisted Web Interview) method, was conducted on a group of 400 active Internet users selected through random and purposive sampling. In the survey, which was carried out in April/May 2022 in Poland, respondents were asked to evaluate cryptocurrencies taking into account the following criteria: familiarity with the concept of cryptocurrency, sources of information about the phenomenon, investment preferences, security awareness, and the respondents' knowledge of the advantages and disadvantages of cryptocurrencies. As a continuation of previous research undertaken in this area, the research demonstrated a strong understanding of the respondents concerning the lack of transparency associated with cryptocurrencies and the cryptocurrency market. The main concerns when using cryptocurrencies are: lack of protection against speculators, lack of supervision by a reputable financial institution, and lack of control of the cryptocurrency market by the National Bank of Poland (NBP). The conclusions and recommendations contained in this article may contribute to increasing knowledge and awareness of the phenomenon analysed, and increasing its popularity in society. Additionally, it provides insights for investors regarding the direction of cryptocurrency development.

Decentralization of Cryptocurrency and Its Theoretical Significance for Economic Efficiency

This paper aims to explore the theoretical significance of the decentralization of cryptocurrency for economic efficiency. The core of the research is to analyze how the decentralization of cryptocurrency affects economic efficiency, especially in terms of changes in transaction costs, resource allocation, and market competition. This paper employs a literature review and theoretical analysis to systematically organize the existing main research findings on cryptocurrencies and their economic effects. During the research process, the basic characteristics and decentralization mechanisms of cryptocurrency were first elaborated in detail, with a focus on the differences from the traditional centralized monetary system. Through theoretical models, this paper discusses how cryptocurrency can enhance economic efficiency by reducing transaction costs, increasing transaction transparency, and reducing intermediary fees. The paper analyzes the potential impact of cryptocurrency on resource allocation, particularly how its decentralization characteristics can drive more effective utilization and distribution of resources. Furthermore, the paper explores the competitive nature of the cryptocurrency market, analyzing the competitive pressure and potential impact on traditional financial markets and institutions. The paper concludes that the decentralization of cryptocurrency has significant theoretical implications for economic efficiency in various aspects, including reducing transaction costs, optimizing resource allocation, and promoting market competition. However, these effects also come with certain risks and challenges, such as market volatility and regulatory complexity. The research in this paper provides a theoretical foundation for understanding the impact of cryptocurrency on economic efficiency and offers references for policymakers in formulating related policies.

Cryptocurrency Adoption in Traditional Banking: Challenges and Opportunities

The advent of cryptocurrencies has introduced a paradigm shift in the financial ecosystem, offering a novel approach to traditional banking and financial transactions. This Master's thesis titled "Cryptocurrency Adoption in Traditional Banking: Challenges and Opportunities" provides a comprehensive analysis of the integration process of cryptocurrencies into the traditional banking sector. It scrutinizes the multifaceted challenges banks face in adopting cryptocurrencies while highlighting the unprecedented opportunities this new form of currency presents. The study commences with a detailed overview of the cryptocurrency market, emphasizing its growth, volatility, and the technological underpinnings that distinguish it from conventional banking systems. It delves into the historical perspective of cryptocurrencies, with a specific focus on Bitcoin, Ethereum, and other significant players, to understand their evolution, current market dynamics, and potential future trajectories.

Decrypting Cryptocurrencies: An Exploration of the Impact on Financial Stability

This study aims to empirically examine the relationship between cryptocurrency and various facets of the financial system. It seeks to provide a comprehensive understanding of how cryptocurrencies interact with, and influence, the stock market, the U.S. dollar’s strength, inflation rates, and traditional banking operations. This is carried out using linear regression models, Granger causality tests, case studies, including the collapse of the Futures Exchange (FTX), and the successful integration of Binance. The study unveiled a strong positive correlation between cryptocurrency market capitalization and key financial indicators like the Dow Jones Industrial Average, Consumer Price Index, and traditional banking operations. This indicates the growing significance of cryptocurrencies within the global financial landscape. However, a mild association was found with the U.S. dollar, suggesting a limited influence of cryptocurrencies on traditional fiat currencies currently. Despite certain limitations such as reliance on secondary data, methodological choices, and geographic focus, this research provides valuable insights for policymakers, financial industry stakeholders, and academic researchers, underlining the necessity for continued study into the complex interplay between cryptocurrencies and financial stability.

Time varying risk aversion and its connectedness: evidence from cryptocurrencies

Changing patterns of risk aversion may follow a non-linear counter-cyclical process. However, the evidence so far has not considered developing cryptocurrency markets. Given some unique features of cryptocurrencies, it is interesting to distinguish how these assets differ from traditional products. This paper investigates the time effects of periodicity on risk aversion for a selection of major cryptocurrencies compared to major financial assets. Significant periodic time-varying patterns are identified when analysing risk aversion. Further, bilateral and bidirectional Granger causalities are identified within cryptocurrencies, as well as between cryptocurrencies and traditional financial assets. Bitcoin is identified as a leading information transmitter of the spillover of risk aversion upon other cryptocurrencies, while estimated risk aversion of traditional financial markets plays a dominant role in the spillover processes upon the cryptocurrency cluster. The latter finding presents further evidence of developing cryptocurrency market maturity. The COVID-19 pandemic is found to have significantly influenced the connectedness of risk aversion among cryptocurrency and traditional financial markets.

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.

Cryptocurrency Price Tracker: A Comprehensive Examination of Market Trends, Real-Time Updates, and User-Centric Features

The cryptocurrency market, characterized by its volatility and rapid fluctuations, necessitates robust tools for tracking and analyzing prices in real-time. This project proposes the development of a cryptocurrency price tracker, leveraging the MERN (MongoDB, Express.js, React.js, Node.js) stack for efficient data management and user interface design. The platform aims to provide users with timely and accurate information on cryptocurrency prices, market trends, and historical data. Key features include real-time price updates, customizable watchlists, advanced charting tools, and secure authentication mechanisms. The project focuses on enhancing user experience, scalability, security, and performance to meet the evolving demands of cryptocurrency enthusiasts and traders. By integrating cutting-edge technologies and adhering to best practices in software development, the cryptocurrency price tracker aims to become a valuable resource for both novice and experienced users navigating the complexities of the cryptocurrency market. Keywords: Cryptocurrency, Price Tracker, MERN Stack, Real-time Data, User Experience, Performance.

Informational Efficiency of Cryptocurrency Markets

Abstract This study employs variance ratios (VRs) to assess the roles of regulation and liquidity on cryptocurrency market efficiency, focusing on crypto-assets subject to varying degrees of regulation. Our findings reveal that cryptocurrencies supervised by FinCEN-licensed exchanges (IEO-L) exhibit market efficiency similar to SEC-regulated traditional stock offerings (IPOs). Conversely, noncompliant crypto-assets display higher market inefficiency. We also establish a connection between regulatory compliance and issuing entity reputation mechanisms. Our results indicate that compliance with existing regulatory norms enhances efficiency and reduces investor risks in crypto-assets. Furthermore, assets voluntarily adhering to regulatory norms can attain efficiency akin to government-regulated assets.