Cryptomarkets Research Articles

Are crypto-investors overconfident? The role of risk propensity and demographics. Evidence from Brazil and Portugal

PurposeThe crypto market is growing quickly, marked by a lack of fundamentals, and the risks are not yet fully comprehended by participants. Our goal is to investigate overconfidence in this market and analyze the role that risk propensity and certain demographics play.Design/methodology/approachWe conducted a survey in Brazil and Portugal, leveraging an online questionnaire disseminated via social media channels to engage a diverse adult population. We collected a total of 826 responses, addressing ethical considerations throughout the process. The data analysis was conducted using SPSS statistical software and logit regression modeling.FindingsOur study reveals that overconfidence is a notable bias that distinguishes individuals who invest in cryptocurrencies from those who do not. Although overconfidence and risk propensity are closely linked, they originate from distinct personal characteristics. Furthermore, our findings indicate that age and market experience positively correlate with overconfidence and negatively correlate with risk propensity. Financial knowledge, interestingly, did not prove to be a significant factor for cryptocurrency investment.Originality/valueOur research augments the existing literature on overconfidence, delving into this phenomenon in a new subdomain, and in doing so, it enriches our comprehension of the unique and still relatively under-researched cryptomarket. Moreover, we illuminate individual factors that sway the decision to invest in cryptocurrencies and should be considered by market participants.Highlights(1)Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey.(2)Verifies the relations among overconfidence, risk propensity, and demographics.(3)Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity.(4)Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies.

Cryptocurrency and Money Laundering

Cryptocurrency, a form of virtual currency, is increasingly pervasive in the modern society. People can use Bitcoin, Ether or Dogecoin to buy a range of products and services with ease and convenience. Yet, beneath the popularity of cryptocurrency is an emerging socio-legal concern: money laundering. Using the information collected from web sources, this brief paper taps into how cryptocurrency money laundering works and what measures can be undertaken to curtail digital crime. In view of the extant information, the paper found that a mixer is required as a ‘middle man’ to convert identifiable crypto-tokens into unidentifiable clean ones thereby delivering them to new wallet(s). In this manner, the origins of these tokens can be obscured, and the new tokens can be accessed and used legally. Many platforms and channels can serve as the mixer to proceed with illegal activities, including casinos, dark web marketplaces, and p2p networks. In considering the ways to tackle such digital crime in jurisdictions with limited oversight, the paper proposes to adopt measures in the Anti-Money Laundering initiatives already implemented in Hong Kong, the United States, and Singapore – especially, the need to strengthen recordkeeping to enhance the traceability and trackability of cryptocurrencies. Meanwhile, laws and rules associated with digital crime shall also be reshaped for risk mitigation.

Investor attention and market activity: evidence from green cryptocurrencies

Purpose This paper aims to investigate the relationship between investor attention and market activity (return, volatility and volume) using a sample of 14 clean energy cryptocurrencies (hereafter green cryptocurrency), namely, Chia, Cardano, Stellar, Tron, Ripple, Nano, IOTA, EOS, Bitcoin Green, Alogrand, Hedara, Polkadot, FLOW and Tezos. Design/methodology/approach This paper use 26040 crypto-day observations and a range of econometric techniques, including Dynamic Granger causality, Panel vector autoregression (VAR), Impulse response function and the decomposition of forecast error variance. Findings Based on 26040 crypto-day observations, this paper finds a bidirectional Granger causal relationship between investor attention and all measures of market activity, namely, return, absolute volatility, squared volatility and volume. The panel VAR and impulse response function demonstrate that market activity in the green crypto ecosystem, especially volatility and volume, is considerably responsive to changes in investor attention proxied by Google search volume (hereafter Google search volume (GSV)). The findings also demonstrate a significant asymmetric effect of return and volume on investor attention since past negative shocks “or bad news” in return and volume are more likely to grab the investor’s attention. All in all, our study emphasizes the crucial role of investor attention in the green crypto ecosystem. Originality/value (i) The research is the first to shed light on investor attention in the green cryptocurrency market. (ii) The paper uses a wide range of green cryptocurrencies to offer a comprehensive picture of the green cryptocurrency ecosystem. (iii) This paper is the first to use the panel Granger causality to investigate investor attention in the cryptocurrency market which provides several advantages over the conventional Granger causality approach. (iv) This paper is the first to provide novel empirical evidence on the prevalent influence of investor attention in the green crypto market.

Optimizing Bitcoin Price Prediction with LSTM: A Comprehensive Study on Feature Engineering and the April 2024 Halving Impact

This research aims to develop a Bitcoin price prediction model using machine learning techniques, with a specific focus on Long Short-Term Memory (LSTM) neural networks. The Bitcoin market is characterized by unique features such as high volatility and the influence of various external factors, which differ significantly from traditional financial markets. As such, precise feature engineering is crucial for accurately modelling Bitcoin prices. Utilizing historical Bitcoin price data from 2014 to 2023, this study extensively evaluates LSTM models. The results indicate that LSTM models provide highly accurate predictions, with a Mean Squared Error (MSE) of 0.0001798 and a Mean Absolute Error (MAE) of 0.0101322. These results demonstrate that LSTM effectively captures the complex and dynamic patterns of Bitcoin prices, outperforming other methods. The findings have significant implications for financial market analysis, especially within the rapidly evolving domain of crypto assets. By leveraging machine learning methodologies, this research enhances understanding of the complexities of the crypto market and offers potential strategies for smarter investment decisions. The success of the LSTM model in improving Bitcoin price prediction accuracy underscores its importance in navigating the volatile and dynamic nature of the crypto market. Overall, this study highlights the substantial potential of machine learning approaches, particularly LSTM models, in analyzing and predicting crypto market behavior. It contributes to the growing academic discourse on the application of advanced technologies in finance and can stimulate further discussions on how machine learning can address challenges and opportunities in the crypto market.

Good vs. bad volatility in major cryptocurrencies: The dichotomy and drivers of connectedness

Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. Our findings indicate that crypto connectedness reflects important events and oscillates substantially while reaching lower limit values when compared to traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that market downturns spill over substantially faster than comparable market surges. Overall, the connectedness dynamics are driven by a combination of both crypto (momentum, on-chain activity, off-chain activity) and legacy financial and economic (financial and economic uncertainty, and financial market performance) factors, while the asymmetry is more connected to the off-chain crypto activity and the combination of economic, financial, and monetary factors. In both the total connectedness and asymmetry modeling, these can serve as hands-on indicators to be further translated into specific portfolio re-balancing decisions, risk management, and regulatory frameworks.

Decrypting Metaverse crypto Market: A nonlinear analysis of investor sentiment

This study aims to investigate the role of investor sentiment in the emerging metaverse market, a novel entrepreneurship model. Empirical analyses are conducted through various causality tests to reveal the predictive power of investor sentiment on the price developments of the metaverse market. The Nonlinear Granger causality test indicates causal effects running from BTC (Bitcoin), GT (Google Trend), and FGI (Fear-Greed Index) to MVI (Metaverse Index). Further examination of these interactions through MS-VAR analysis reveals that under bear market regimes, both investor sentiment proxies (GT and FGI) and BTC have a statistically significant causal effect on the returns of MVI. This finding suggests that metaverse crypto market returns are substantially influenced by investor sentiment during periods of anxiety and turmoil, evident in steep bear markets, rather than during periods of tranquility and euphoria characteristic of bull markets. The results of the time-varying approach confirm this finding by indicating spikes in causal effects towards the end of 2021, during which a severe crash in cryptocurrency markets occurred. Overall, the causal links during market downturns may stem from the fear of missing out (FOMO) in retail investors, who mainly dominate the sentimental factors utilized in this study.

Digital Markets Dynamics and Ethical Considerations of Online Drug Sales via Cryptomarkets

The article is devoted to a topical topic – the analysis of a new way of selling drugs via the Internet and using cryptocurrency. In the article, the authors analyzed the Internet as a medium for drug trafficking; the composition of the criminal offense, which consists of the illegal sale of drugs; the social conditionality of criminalization of drug trafficking via the Internet; trends in the use of cryptocurrencies for drug trafficking; money laundering from drug trafficking through cryptocurrencies and measures to combat drug trafficking online and through cryptocurrency. It is emphasized that the appearance of this way of selling drugs is due to the development of technologies and their undoubted advantages, which criminals, from small fraudsters to criminal groups, adapt to carry out illegal activities. The number of people who buy illegal drugs on the dark web is gradually increasing; more and more buyers prefer to pay with cryptocurrency, and criminals are trying to legalize the proceeds of drug crime through the crypto market. All this indicates the increased social danger of such drug trade compared to other forms of their sale due to significant profits, the involvement of a large number of consumers, and direct and hidden effects on the growth of a country: public health issues, organized crimes, security concerns, etc. However, the fight against drug trafficking on the Internet and with the use of crypto-currency has not been properly regulated at the national and international levels. Considering this to fight against such actions effectively, the following is necessary today: an official definition of the status of cryptocurrency in legislation and the development of new methods, tactics, and technologies for the investigation of crimes. Drug business on the Internet and through cryptocurrencies, which will consider the latter's peculiarities and will be constantly updated in accordance with the pace of evolution of digital currencies; establishment of cooperation of cryptocurrency exchanges, financial institutions, regulators, law enforcement agencies, and technology providers.

Prediksi Harga Crypto dengan Algoritma Jaringan Saraf Tiruan

Cryptocurrency has emerged as a crucial element in the global financial market, prompting investor interest in predicting prices for informed investment decision-making. However, the highly volatile nature of cryptocurrency prices has made trading in the crypto market speculative and laden with significant risks. Therefore, this study aims to apply Artificial Neural Networks (ANNs) using the Long Short-Term Memory (LSTM) algorithm to forecast cryptocurrency prices. This method leverages the ANN's ability to recognize complex patterns and trends in historical cryptocurrency price data. The research findings demonstrate significant accuracy levels for three types of crypto: BTC (86.86%), BNB (96.8%), and Doge (97%). Evaluation was conducted using the k-fold cross-validation method with k=5, where data was divided into five equally sized groups. Accuracy was computed by comparing actual prices with those predicted by the LSTM model. This evaluation approach provides robust insights into the LSTM model's effectiveness in predicting cryptocurrency prices, considering significant data variations. The predicted values from this study are BTC at $4306, BNB at $58.7, and Dogecoin at $0.037. These implications underscore the critical role of the number of epochs in influencing the performance and accuracy of cryptocurrency price predictions. Thus, this research is poised to offer deeper insights into cryptocurrency market behavior and provide practical guidance for investors to make more informed and measured investment decisions.

GOVERNMENT DEBT AND CRYPTOCURRENCIES: HOW BITCOIN IS CHANGING THE RULES OF THE GAME

In recent years, there has been a significant increase in public debt in many countries, which calls into question the sustainability of traditional financial systems. In this context, cryptocurrencies, especially bitcoin, are beginning to play an increasingly important role in the economy. This article explores the relationship between government debt and cryptocurrencies, focusing on how bitcoin can change the rules of the game in the financial world. We consider the mechanisms by which bitcoin and other cryptocurrencies can serve as an alternative to traditional financial instruments, as well as their potential as a means of protection against inflation and devaluation. In addition, the risks and benefits of investing in cryptocurrencies are analyzed against the background of growing government debt. The article also discusses the impact of regulation and government control on the development of the crypto market and its interaction with public finances. In conclusion, we hypothesize about the future role of bitcoin in the global economy and its ability to change the approach to public debt management.

User Acceptance of Blockchain Technology in Financial Applications: Information Security, Technology Awareness and Privacy Aspects

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.

Is crypto legitimate? Study on the relationship between legitimacy and public engagement on crypto market in Indonesia

Is crypto legitimate? Study on the relationship between legitimacy and public engagement on crypto market in Indonesia

Advanced Statistical Analysis of the Predicted Volatility Levels in Crypto Markets

Our paper deals with an advanced statistical tool for the volatility prediction problem in financial (crypto) markets. First, we consider the conventional GARCH-based volatility models. Next, we extend the corresponding GARCH-based forecasting and calculate a specific probability associated with the predicted volatility levels. As the probability evaluation is based on a stochastic model, we develop an advanced data-driven estimation of this probability. The novel statistical estimation we propose uses real market data. The obtained analytical results for the statistical probability of the levels are also discussed in the framework of the integrated volatility concept. The possible application of the established probability estimation approach to the volatility clustering problem is also mentioned. Our paper includes a concrete implementation of the proposed volatility prediction tool and considers a novel trading and volatility estimation module for crypto markets recently developed by the 1ex Trading Board group in collaboration with GoldenGate Venture. We also briefly discuss the possible application of a model combined with the data-driven volatility prediction methodology to financial risk management.

Assessing network risk with FRM: links with pricing kernel volatility and application to cryptocurrencies

The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. FRM @ Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market.

Modelling Asymmetric Volatility in the Crypto Currency and its Dynamic Relationship with Stock Market

The paper investigates the asymmetric volatility effect of five major cryptocur- rencies and their bilateral linkages with major indices in the Indian stock market. To inves- tigate the bilateral relationship of crypto currency with stock indices, researchers used two major stock indices in the Indian stock market namely, BSE Sensex and NSE Nifty. The study used GARCH, EGARCH and TGARCH models, asymmetric to model the asymmetric volatility effect in the conditional volatility of crypto currencies and stock indices. Johansen’s cointegration and Vector Error Correction Model is used for examining the presence of cointe- gration between selected variables and to analyse the strength of causality among them. The study finds the evidence of cointegration between cryptocurrencies and stock market indices, implying that cryptocurrencies are related to stock indices. Further there is unidirectional relationship among stock and crypto market and crypto currencies having short-lived re- sponse to shocks in stock markets. Even with these currencies’ explosive growth, there are still not many research examining their connection to stock markets. This study will help investor’s those who making investment in currency market or in the stock market to eval- uate the pattern of volatility, interconnection among them, so that they can make crucial investment decisions and diversification strategies. This will help them to gain knowledge about how these two markets move together so they may avoid underestimating risk when building portfolios that contain both kinds of assets.

Evolving Efficiency of Cryptocurrency Market: Evidence from Leading Cryptocurrencies

The article investigated the efficiency of the cryptocurrency market, with a focus on Bitcoin (BTC), Ethereum (ETH), Tether (USDT), and Binance Coin (BNB-USD), based on their substantial market capitalization. For this purpose, we observed the daily closing prices spanning from January 2018 to December 2023 and employed a set of robust tests, including the generalized spectral test, automatic portmanteau test, and automatic variance ratio test. The findings of the study reveal a random pattern in price fluctuations, indicating weak form efficiency. Furthermore, we adopted the rolling window approach to investigate whether market efficiency is dynamic or static over time. The empirical result illustrated that the crypto market efficiency remains static over time, except for USDT. In conclusion, the overall empirical results support the notion of the random walk hypothesis, indicating that past price movements offer no predictive insight into future prices. These findings have significant implications for investors, emphasizing the lack of predictive insight from past price movements. Policymakers are urged to establish a robust framework for market integrity and reliable price discovery.

The AI revolution: are crypto markets more efficient after ChatGPT 3?

This study examines the efficiency of crypto markets in the context of the AI revolution, focusing on AI-Crypto sectors. By employing the Adjusted Market Inefficiency Magnitude (AMIM) and conducting quantile efficiency and liquidity analyses, we observe dynamic fluctuations in market efficiency across sectors like Generative AI, AI Big Data, Cybersecurity, and Distributed Computing. Our findings reveal that most AI-Crypto sectors present a tendency towards higher efficiency in extreme market conditions. The introduction of ChatGPT 3 significantly enhanced market efficiency, with sectors associated with AI experiencing positive mean returns and increased liquidity. Our results suggest that market efficiency in these sectors is not static but evolves with technological innovations and sector-specific characteristics, in that AI-related sectors are driving the recent market dynamics, with increased liquidity and efficiency observed post-ChatGPT 3 launch. This research extends previous work on crypto market efficiency and explores the incorporation of information in AI tokenized projects, underscoring the transformative impact of AI on the crypto market landscape.

Contribution to the harm assessment of darknet markets: topic modelling drug reviews on Dark0de Reborn

Amid the global opioid crisis, the volume of drug trade via darknet markets has risen to an all-time high. The steady increase can be explained by the reliable operation of darknet markets, affected by community-building trust factors reducing the risks during the process of the darknet drug trade. This study was designed to explore the risk reduction efforts of the community of a selected darknet market and therefore contribute to the harm assessment of darknet markets. We performed Latent Dirichlet Allocation topic modelling on customer reviews of drug products (n = 25,107) scraped from the darknet market Dark0de Reborn in 2021. We obtained a model resulting in 4 topics (coherence score = 0.57): (1) feedback on satisfaction with the transaction; (2) report on order not received; (3) information on the quality of the product; and (4) feedback on vendor reliability. These topics identified in the customer reviews suggest that the community of the selected darknet market implemented a safer form of drug supply, reducing risks at the payment and delivery stages and the potential harms of drug use. However, the pitfalls of this form of community-initiated safer supply support the need for universally available and professional harm reduction and drug checking services. These findings, and our methodological remarks on applying text mining, can enhance future research to further examine risk and harm reduction efforts across darknet markets.

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.

US macroeconomic determinants of Bitcoin

This study aims to determine the impact of macroeconomic variables on bitcoin prices in the United States. Bitcoin is one of the cryptocurrencies that has the highest price and the most users in the United States in recent years. This study uses monthly data on inflation, interest rates, USD/EUR rates, gold prices, and bitcoin prices. To achieve the objectives of this study, Dynamic Conditional Correlation (DCC) and Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) were used. The results showed that there is a negative and significant relationship between the variables of inflation, interest rates, and USD/EUR rates affecting the price of Bitcoin in that period. Conversely, there is a positive and significant relationship between the price of gold and the price of Bitcoin in the United States during that period. An in-depth understanding of how macroeconomic factors such as inflation, interest rates and the USD/EUR rates affect Bitcoin price is key to making smart investment decisions in an increasingly complex crypto market. The findings of this analysis confirm that the significant relationship between macroeconomic variables and Bitcoin price provides deeper insights for investors to anticipate market movements and design adaptive investment strategies.