This study aims to analyze the impact of internal variables, including total Ethereum, number of transactions, fees per transaction, and number of active wallets, as well as external variables, namely the price of Bitcoin and the price of gold, on global Ethereum prices. The study utilizes daily data covering the period from December 31, 2016, to December 31, 2021. The data analysis employs time series data with the assistance of Eviews 10 and the error correction model (ECM) method. The study's findings indicate that total Ethereum, number of transactions, fees per transaction, number of active wallets, price of Bitcoin, and price of gold collectively exert a significant influence on Ethereum prices. However, when examined individually, total Ethereum demonstrates a negative impact and lacks statistical significance on Ethereum prices. Similarly, the number of transactions exhibits a negative and significant effect on Ethereum prices. Conversely, transaction fees, number of active wallets, and the price of Bitcoin have a positive and significant impact on Ethereum prices. Meanwhile, global gold prices do not exhibit any influence on Ethereum prices.
Nektarios Aslanidis, Aurelio F. Bariviera, Christos S. Savva
This paper adopts a versatile conditional correlation approach to explore daily seasonality in the major cryptocurrencies. Given the lack of clear fundamental value in this market and the active online profile of investors, the study also relates cryptocurrency cross-correlations to online market attention and sentiment. Our results highlight that while investor attention has a positive effect, sentiment has a much stronger negative impact on the correlations. These findings can offer interesting insights for investors and regulators, as the influence of market attention and sentiment on the correlations has important implications for portfolio diversification and market stability.
Purpose: This study sought to explore cryptocurrency and its role in portfolio diversification. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to cryptocurrency and its role in portfolio diversification. Preliminary empirical review revealed that incorporating cryptocurrencies into investment portfolios offered promising diversification benefits due to their low correlation with traditional assets, despite their high volatility and regulatory uncertainties. It highlighted the significant risk management challenges posed by cryptocurrencies' extreme price fluctuations and the evolving regulatory landscape. The study emphasized the importance of careful, limited allocation to cryptocurrencies, robust risk management practices, and continuous market monitoring. Ultimately, it suggested that cryptocurrencies could enhance portfolio performance when strategically used alongside traditional diversification methods. Unique Contribution to Theory, Practice and Policy: The Modern Portfolio Theory, Efficient Market Hypothesis and Behavioural Finance Theory may be used to anchor future studies on portfolio diversification. The study recommended a cautious yet strategic inclusion of cryptocurrencies in investment portfolios to enhance diversification, emphasizing the importance of ongoing research, robust risk management, and proactive monitoring due to their high volatility and regulatory uncertainties. It called for clear and consistent regulatory frameworks to protect investors while fostering market growth, and highlighted the need for collaboration between academia, industry, and regulatory bodies to improve financial literacy and market stability. These recommendations aimed to contribute to theoretical, practical, and policy aspects of cryptocurrency investments.
This article comprehensively explores multiple aspects of cryptocurrencies and their price forecasting. Firstly, the article introduces the definition of cryptocurrency and its development process on a global scale, especially focusing on the launch of Facebook Libra and China's central bank digital currency, highlighting the importance and influence of digital currency in the global financial market. Subsequently, the article analyzes the advantages of digital currencies over traditional currencies, including improving economic transaction efficiency, reducing transaction costs and enhancing transaction transparency. Meanwhile, the article also explores the challenges and risks potentially brought by the development of digital currencies, such as regulatory uncertainty and market volatility. In this context, the article raises the importance of cryptocurrency price forecasting and introduces the forecasting models and techniques commonly used today. Finally, through specific experimental analysis, the effectiveness of using the deep learning model CNN-LSTM to predict the price of Bitcoin is demonstrated, and the directions of future research and optimization strategy are proposed. In summary, this paper comprehensively presents the research status and prospects of cryptocurrency and its price prediction field through systematic introduction and analysis.
The study explores diverse AI methodologies employed in the cryptocurrency domain, focusing on their applications in key areas such as price prediction, sentiment analysis, market trend analysis, volatility prediction, trading strategy optimization, fraud detection, and portfolio management. Various machine learning models, including regression, neural networks, and reinforcement learning, are investigated for their effectiveness in predicting cryptocurrency prices and optimizing trading strategies. The integration of Natural Language Processing (NLP) techniques is discussed in the context of sentiment analysis, where AI algorithms analyze vast amounts of textual data from social media, news articles, and online forums to gauge market sentiment and its potential impact on cryptocurrency prices. Additionally, the paper examines the role of AI in identifying patterns, trends, and anomalies in market data, facilitating effective decision-making for traders and investors. However, the paper emphasizes the need for caution, acknowledging the inherent uncertainties and risks associated with cryptocurrency investments. It concludes by highlighting the potential for continued advancements in AI applications, contributing to a deeper understanding of cryptocurrency market dynamics and aiding in more informed decision-making in this rapidly evolving financial landscape.
Our study investigates the hedging ability of Gold and Bitcoin to hedge against financial market crashes. We also examined the ability of the VIX fear gouge to improve the ability of those financial assets to hedge financial risks. We found a positive dependency between the current daily prices of Gold and Bitcoin with a stronger impact of Gold on Bitcoin than vice versa. We also find that in recent years (2021-2023), Gold price changes are negatively correlated to yesterday's price change of the S&P500 a day before and positively correlated to yesterday's NASDAQ price change.
An Pham Ngoc Nguyen, Martin Crane, Thomas Conlon, Marija Bezbradica
Herding behavior has become a familiar phenomenon to investors, with potential dangers of both undervaluing and overvaluing assets, while also threatening market stability. This study contributes to the literature on herding behavior by using a recent dataset, covering the most impactful events of recent years. To our knowledge, this is the first study examining herding behavior across three different types of investment vehicle and also the first study observing herding at a community (subset) level. Specifically, we first explore this phenomenon in each separate type of investment vehicle, namely stocks, US ETFs and cryptocurrencies, using the Cross-Sectional Absolute Deviation model. We find mostly similar herding patterns for stocks and US ETFs. Subsequently, the same experiment is implemented on a combination of all three investment vehicles. For a deeper investigation, we adopt graph-based techniques including the Minimum Spanning Tree and Louvain community detection to partition the combination into smaller subsets to detect herding behavior for each subset. We find that herding behavior exists at all times across all types of investment vehicle at a subset level, although perhaps not at the superset level, and that this herding behavior tends to stem from specific events that solely impact that subset of assets. Lastly, we explore herding by examining the financial contagion effects between these types of investment vehicle. Results show that US ETFs not only have a tendency to propagate similar trading behaviors in stocks and especially cryptocurrencies but also show self-reinforcing herding behavior, acting as drivers of their own trends.
This paper examines the efficiency and asymmetric multiracial features of NFTs (Mana, Tezos), and traditional assets (EGX30, Oil index) using Asymmetric Multiracial Cross-Correlations Analysis covering the period from January 2020 to May 2021. Considering the full sample with a significant variation among asset classes. (Oil-Tezos)and (Mana-Tezos) is the most efficient.Since their inception, the blockchain-based digital asset classes have received immense interest from investors and portfolio managers as an alternative investment platform. Along with other established traditional cryptocurrencies such as Bitcoin, Litecoin, Ripple, and Ethereum, new blockchain asset classes such as Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) have made a considerable contribution tothe asset market’s recent expansion (Aharon & Demir, 2021; Alam, Chowdhury, Abdullah, & Masih, 2023; Maouchi, Charfeddine, & el Montasser, 2021; Yousaf & Yarovaya, 2022).Fundamentally, NFTs and DeFi differ from traditional cryptocurrencies as they are not virtual currency. Where NFTs are non-transferable cryptographic digital assetscreated by Ethereum smart contracts and can be sold and traded, the interchangeability of NFTs when comparing the other cryptocurrencies is very low (Karim, Lucey, Naeem, & Uddin, 2022; Q. Wang, Li, Wang, & Chen, 2021; Y. Wang, 2022).The NFTs and DeFi are relatively contemporary and unexplored asset classes, but their market capitalization has grown substantially as risk minimizing assets, particularly during the COVID-19 period. In the NFT space, the KeywordsVolatility, NFTs, Traditional Financial Assets, and MGARCH
Oleg P. Kultygin, Natalia N. Lyublinskaya, Elena N. Tokmakova, Alexander E. Trubin
The article deals with the research on global crypto-currency market and analysis of its development trends. Special attention was paid to national regulation of crypto-currency and activity connected with it, which is topical today due to introduction of new sanctions against Russia, including those in the field of crypto-exchanges. Through analyzing the development of finance blockchain-systems the authors identified the vector of their developing in the direction of raising the speed of work, upgrading algorithms of consensus, increasing security and control over the mining sphere and turnover of crypto-currency in Russia. A conclusion was drawn that it can foster the extended use of crypto-systems of distributed ledger by present day banks. As goals of the research the authors mentioned problems connected with the opportunity to use blockchain technologies in the credit and finance sector and forecasting effects of such use.
Volatility as a measure of financial risk is a crucial input for hedging, portfolio diversification, option pricing and the calculation of the value at risk. In this paper, we estimate the asymmetric and time-varying volatility for Bitcoin as the dominant cryptocurrency in the world market. A novel approach that explicitly separates the falling markets from the rising ones is utilized for this purpose. The empirical results have important implications for investors and financial institutions. Our approach provides a position-dependent measure of risk for Bitcoin. This is essential since the source of risk for an investor with a long position is the falling prices, while the source of risk for an investor with a short position is the rising prices. Thus, providing a separate risk measure in each case is expected to increase the efficiency of the underlying risk management in both cases compared to the existing methods in the literature.
Hamid Cheraghali, Péter Molnár, Mattis Storsveen, Florent Veliqi
We investigate the impact of cryptocurrency-related cyberattacks on the cryptocurrency market and traditional financial markets. The dataset consists of historical cyberattack data and trading data for twenty cryptocurrencies, three cryptocurrency uncertainty indices, five payment companies, four stock indices, a commodity index, and gold. We find that cyberattacks are associated with negative returns, increased volatility, and increased trading volume not only for the cryptocurrencies but also for the payment companies, the financial and technology sectors, and the general stock market. However, the impact of cyberattacks on cryptocurrencies has been decreasing over time, while the impact on payment companies and the financial sector has been increasing. Moreover, gold prices have shown a positive response to these cyberattacks. These results underscore the need for enhanced cybersecurity measures in the fintech sector and may inform both policymakers and market participants.
Shinta Amalina Hazrati Havidz, Maria Divina Santoso, T. Alexander, Caroline Caroline
Purpose This study aims to identify the financial attributes of non-fungible tokens (NFTs) as safe havens, hedges or diversifiers against traditional (stock indices, foreign exchange, gold and government bonds) and digital (Bitcoin and Ethereum) assets. Design/methodology/approach The quantile via moments was utilized, and the data spanned from 20 September 2021 to 31 January 2022. The authors incorporated feasible generalized least squares (FGLS) and difference-generalized method of moments (diff-GMM) as the robustness check. Findings Overall, NFTs offer strongly safe havens, hedging and diversifier attributes against cryptocurrencies, while weak properties for traditional assets. The specific findings are: (1) Bored Ape Yacht Club (BAYC) serves as a strong hedge for Bitcoin during market rise; (2) Mutant Ape Yacht Club (MAYC) serves as a strong safe haven against Bitcoin during market bull; (3) Crypto punk (CP) provides strong safe havens properties for gold during market turmoil while serving as a strong hedge against gold and Bitcoin on average and (4) the three blue-chip NFTs are powered by Ethereum blockchain, thus serving as a diversifier against Ethereum. Practical implications Bitcoin investors are suggested to include NFTs in their investment portfolio to mitigate the losses when Bitcoin falls. Meanwhile, the inclusion of crypto punk is advised for risk-averse investors who invest in gold. NFTs are powered by the Ethereum blockchain, indicating co-movement among them and thus, serve as diversifiers. Policymakers and regulators are suggested to watch closely over NFTs' great development and restructure the existing policies and thus, stabilization of asset markets can be achieved. Originality/value The originality aspects are: (1) focusing on the three blue-chip NFTs (i.e. BAYC, MAYC and CP) that are categorized as the largest NFTs by floor market capitalization; (2) testing the NFT attributes (safe havens, hedges or diversifiers) against traditional and digital assets, a.k.a., cryptocurrencies and (3) panel setting on 14 countries with the highest NFT users.
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.
Taha Zaghdoudi, Kais Tissaoui, M. Maâloul, Younés Bahou · 5 authors
This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoin’s energy consumption. Three machine learning tools, SVR (scikit-learn 1.5.0),CatBoost 1.2.5 and XGboost 2.1.0, are used to evaluate the complex relationship between uncertainty indices and bitcoin’s energy consumption. Results reveal that the XGboost model outperforms both SVR and CatBoost in terms of accuracy and convergence. Furthermore, the feature importance analysis performed by the Shapley additive explanation (SHAP) method indicates that all uncertainty indices exhibit a significant capacity to predict bitcoin’s future energy consumption. Moreover, SHAP values suggest that economic policy uncertainty captures valuable predictive information from the energy uncertainty indices and geopolitical risks that affect bitcoin’s energy consumption.
Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices.
Recent studies document that cryptocurrencies offer an alternative store of value, medium of exchange and can be used to hedge against currency and price fluctuations. However, the frequent collapse of the crypto-market undermines its safe-haven characteristics, as investors’ fear and anxiety could intensify market volatility and trigger a financial crisis. Motivated by the current global vicissitudes, this study examines the impact of uncertainty and sentiment factors on price behaviour of cryptocurrencies. To estimate our model, we used daily, low, high and closing price data for major crypto projects, from January 2018 to January 2023. We show that economic and political uncertainty factors significantly drive crypto prices. Furthermore, the interaction between sentiment dynamics as expressed by investors on different social platforms has a significant adverse effect on the returns of the cryptocurrency market, and the impact is more pronounced for tokens within the same ecosystem. Using the asymmetric GARCH-MIDAS model and TVP-VAR, we also demonstrate the existence of a significant contagion among tokens within the same ecosystem when bad (or good) news occurs. Considering the massive unprotected losses incurred by crypto investors during crises, our results provide important insights into how portfolio managers can effectively design investment strategies.
A. M. Benarous, İ̇hsan Tolga Medeni, Tunç D. Medeni, Vildan Ateş
This study sheds light on the achievements of digital financial technologies and blockchain technology in the stock market. This study aims to examine the relationship between blockchain technology and macroeconomic variables, as well as the impact these variables have on stock market performance. For this, authors used the methodology of correlation and regression analysis, analyzing data on cryptocurrencies, the stock market and key paper exchange rates. The study confirms a significant correlation between blockchain dynamics, particularly cryptocurrency price fluctuations, and stock market performance, indicating that movements in digital asset classes such as Bitcoin and Ethereum have measurable impacts on traditional financial markets. Traditional economic indicators continue to play a crucial role in stock market behavior, with variables like inflation rates and GDP growth showing strong correlations with market performance. The results suggest a complex interplay between blockchain technology and macroeconomic indicators, emphasizing a growing interconnectedness between emerging digital financial products and economic measures. In addition, the findings are particularly relevant for investors, financial analysts, and policymakers, highlighting the need for a holistic market analysis approach that integrates both new technological advancements in blockchain and economic indicators. The study underscores the evolving influence of blockchain technology on traditional stock markets that encompass both new digital assets and economic frameworks. Moreover, further studies could explore the impact of blockchain technology on specific sectors within the stock market, such as technology, finance, and consumer goods.
This article is dedicated to a detailed analysis of market cycles in cryptocurrencies and their impact on investment strategies. The article thoroughly examines various stages of these cycles, their characteristics, and their interconnections with other economic factors. It explores the factors influencing the duration and intensity of these cycles, as well as methods of utilizing them to develop successful investment strategies. The research findings highlight the importance of understanding psychological factors such as FOMO (fear of missing out) and FUD (fear, uncertainty, and doubt), as well as the impact of halving on the cryptocurrency market. Investors who comprehend these aspects and adapt their strategies to the volatile market conditions can achieve success in their investments. The article also emphasizes the importance of in-depth analysis of market cycles for developing effective investment strategies. Special attention is given to the stages of accumulation, markup, distribution, and markdown in the cryptocurrency market, each of which has its unique characteristics and can be leveraged for profit maximization. The influence of regulatory changes, technical innovations, and global financial events on these cycles is examined. The authors also analyze the interaction of supply and demand, particularly how the reduction in mining rewards (halving) affects cryptocurrency values. The study shows that understanding market cycles allows investors to better predict market movements and make more informed decisions. Examining the impact of psychological factors on investor decisions is crucial for avoiding unjustified losses and maximizing gains. Additionally, the article considers long-term investment strategies that take into account halving periods, which can lead to significant increases in asset values. Based on the analysis of cryptocurrency market cycles and the influence of various factors, the work concludes that a deep understanding of these processes is necessary for successful investing. The recommendations provided in the article can be useful for investors looking to develop resilient and effective strategies in the highly volatile cryptocurrency market.
In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.
Dzuljastri Bin Abdul Razak, Mustafa Omar Mohammed, Yavuz Türkan, Ethem KILIÇ
With technology development, investment tools also vary. Money and capital market instruments are at the forefront of these, and virtual currencies have become investment tools. Because virtual currencies are not religiously permissible by many organizations causes the devout people to stay away from them. This study investigates the return and volatility interaction between Islamic Indices and Bitcoin in Türkiye and Malaysia. The study uses weekly data for the period 24 November 2013 – 2 January 2022 obtained from investing.com. Multivariate Dynamic Conditional Correlation (DCC-GARCH) and multivariate dynamic stochastic volatility models were used to determine the volatility dispersion between Islamic indices and Bitcoin. Results show that the volatilities of Türkiye Islamic Index, Malaysia Hijrah Shariah Index and Bitcoin are permanent. Volatility of Bitcoin, however, has no effect on the return of the Türkiye Islamic Index and the Malaysian Hijrah Shariah Index. Likewise, the volatility of Islamic indices does not affect the return of Bitcoin. According to the results of the DC-MSV model, the volatility of Islamic indices and the volatility of Bitcoin do not affect each other. This indicates that Islamic index investors and Bitcoin investors differ.
Non-fungible tokens are transferable rights to digital assets such as artwork, videos, in-game items, collectibles or music. Non-fungible tokens relate only to a specific unique item and carry information about the owner. The non-fungible token market has received widespread attention and has grown enormously since the beginning of 2021. Despite significant growth in the market, there needs to be more surveys, especially in the context of the Czech Republic. This article, therefore, aims to evaluate the level of awareness of non-fungible tokens in the Czech Republic. The paper presents the basics of the non-fungible token market, its potential and uncertainty, and the interdisciplinary nature of non-fungible token research. First, the characteristics of non-fungible tokens are described based on a literature review. The methodological part outlines an empirical analysis based on a quantitative survey in which 103 respondents in the Czech Republic took part. Based on the research results, it was found out that in the Czech Republic, there is low level of awareness of non-fungible tokens and also low level of trust in digital assets in general. In conclusion, it is possible to say that this article provides an overall understanding of the phenomenon of non-fungible tokens in the Czech Republic.
To what extent does the collapse of a digital token spread contagion across cryptocurrency markets? How do markets incorporate information in this turbulent setting? We examine contagion effects across major digital exchanges during the collapse of the FTX exchange and its token, FTT. We find evidence of contagion across crypto exchanges. We also examine the information cascade effects of other crypto assets on FTX when nearly all withdrawals were prohibited. We find abnormal returns for major assets, indicating a flight to safety from less to more authoritative digital assets. The implications for traders, exchanges, and policymakers are discussed.
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.