Cryptocurrency Market Research Articles

The Impact of the Cryptocurrency Market on Islamic vs. Conventional Stock Returns: Evidence from Gulf Cooperation Council Countries

The rapid rise and widespread global adoption of cryptocurrencies in recent years has fundamentally transformed the international financial landscape, with digital assets increasingly being recognized for their potential to influence the stability and performance of traditional capital markets. Against this backdrop, this study aims to empirically investigate the impact of cryptocurrency returns on Islamic vs. conventional stock returns in Gulf Cooperation Council (GCC) countries. The salient distinctions between Islamic and conventional stock markets include fundamental differences in principles, investment allocations, and risk profiles, underscoring the importance of examining the impact of cryptocurrency returns on these distinct equity segments. Daily data were collected from stock indices in five GCC countries over the period 2016–2019, including two sub-periods: before and after the 2017 crypto crash. Pooled OLS, fixed effects, random effects, and generalized linear models (GLMs) were used to analyze the data collected during the study. With the GCC increasingly focusing on cryptocurrency markets, there is growing concern about these markets’ potential impact on regional stocks. This study addresses the important questions of whether the impacts of the cryptocurrency market on Islamic vs. conventional stock markets differ throughout the GCC region and how these impacts have evolved since the crypto crash period. The findings reveal that cryptocurrency returns had a negative impact on both GCC Islamic and conventional stock market returns for the full sample period (2016–2019), and the negative effect was far more pronounced for conventional stocks. For the two sub-periods before and after the crash, only the cryptocurrency market and conventional GCC stocks remained negatively correlated, while the cryptocurrency market and the GCC Islamic stock markets became uncorrelated. Thus, for the calmer sub-periods before and after the crypto crash, the rise in cryptocurrency returns may have enticed GCC investors away from conventional stocks, perhaps resulting in a decline in their investment in these stocks. Meanwhile, those who invest in Islamic stocks may not be exposed to this temptation.

Industrial metal and cryptocurrency market plummets: Interdependence, policy uncertainty, or investor sentiments?

Industrial metal and cryptocurrency market plummets: Interdependence, policy uncertainty, or investor sentiments?

AI Applications in Analysing and Predicting Cryptocurrency Market

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.

Effectiveness of Using Candlestick Charts to Forecast Ethereum Price Direction: A Machine Learning Approach

Cryptocurrency is a form of decentralized digital currency. Ethereum is the second-largest cryptocurrency by market capitalization and the largest altcoin. Cryptocurrencies including Ethereum are highly volatile. Hence, shortterm directional forecasts in the cryptocurrency market have become a widely discussing topic. Candlestick charts are useful visualizations of the open, high, low and close prices which can identify patterns and gauge the near-term direction of prices. This research explores the effectiveness of forecasting hourly Ethereum closing price direction based on candlestick charts within a short time horizon. The proposed forecasting algorithm incorporates clustering methods such as fuzzy K-means, K-means and partition around medoids clustering to cluster candlestick chart properties namely upper shadow length, body length and lower shadow length. Classification methods such as random forest, support vector machine and K-nearest neighbour were used to forecast closing price direction using 16 different predictor variable sets including open, high, low and close prices, candlestick chart price direction, USL, BL and LSL. The accuracy for all considered cases was around 50%. Clustering improved the accuracy slightly and including the CPD with the predictor variable sets under consideration can increase the accuracy slightly. However, this approach is performing better in predicting the Down cases to the total number of actual Down cases because there is a higher sensitivity of 81.20% based on the SVM with Open, High, Low and Close at t in the clustering ignored method.

The role of promotion versus prevention-orientation to predict individual cryptocurrency participation

We investigate cryptocurrency participation within the context of Regulatory Focus Theory (RFT) among 1,519 individuals in Denmark, Finland and Sweden. Analysis of survey responses identifies the tendency for promotion-focused investors to gravitate towards the high-risk, high-reward potential of cryptocurrencies, while prevention-focused investors adopt a more cautious approach that prioritizes safer assets. Our results which demonstrate that RFT can be used to predict current and future cryptocurrency participation contribute to understanding the profile of participants in the rapidly growing cryptocurrency market.

Hybrid Optimal Time Series Modeling for Cryptocurrency Price Prediction: Feature Selection, Structure and Hyperparameter Optimization

The prime aim of the research is to forecast the future value of bitcoin that is commonly known as pioneer of the Cryptocurrency market by constructing hybrid structure over the time series. In this perspective, two separate hybrid structures were created by using Artificial Neural Network (ANN) together with Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO). By using the hybrid structures created, both the network model and the hyper parameters in the network structure, together with the time intervals of the daily closing prices and how many data should be taken retrospectively, were optimized. Employing the created GA-ANN (DCP1) and PSO-ANN (DCP2) hybrid structures and the 721-day Bitcoin series, the goal of accurately predicting the values that Bitcoin will receive has been achieved. According to the comparative results obtained in line with the stated objectives and targets, it has been determined that the structure obtained with the DCP1 hybrid model has a success rate of 99% and 97.54% in training and validation, respectively. It should also, be underlined that the DCP1 model showed 47% better results than the DCP2 hybrid model. With the proposed hybrid structure, the network parameters and network model that should be used in the ANN network structure are optimized in order to obtain more efficient results in cryptocurrency price forecasting, while optimizing which input data should be used in terms of frequency and closing price to be chosen.

The impact of cryptocurrency-related cyberattacks on return, volatility, and trading volume of cryptocurrencies and traditional financial assets

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.

Challenges and Prospects of Crypto-Currency Market Development

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.

Negative impact of Ponzi Schemes on Crypto-market

Ponzi schemes have had a profoundly negative impact on the cryptocurrency market, eroding investor confidence, attracting stringent regulatory scrutiny, and contributing to significant market volatility. High-profile scams such as BitConnect have led to substantial financial losses, fostering skepticism towards new and legitimate cryptocurrency projects. The increased regulatory scrutiny resulting from these schemes has led to stricter regulations, potentially stifling innovation and growth within the market. Additionally, the negative media coverage associated with Ponzi schemes perpetuates the perception that the cryptomarket is rife with fraud, deterring mainstream adoption and fostering a biased public view. This paper explores the multifaceted negative impacts of Ponzi schemes on the cryptomarket, highlighting their role in undermining trust, instigating legal repercussions, and causing market instability. The findings underscore the need for enhanced regulatory frameworks and investor education to mitigate the risks associated with Ponzi schemes and restore confidence in the burgeoning field of cryptocurrency.

Quantitative Analysis of the Relationship Between Cryptocurrency Market and U.S. Stock Market Performance

With particular attention to variables like volatility and the performance of the U.S. stock market, this study attempts to conduct a thorough quantitative examination of the relationship between the cryptocurrency market. By using sophisticated mathematical modeling approaches, such as regression analysis and correlation methodologies, it is hoped to identify the key characteristics of these markets as well as the degree to which cryptocurrency volatility and stock market success are causally related. The use of historical data, spanning a specific time (from July 1st, 2019, to July 1st, 2023) around 4 index price-day transaction data will be made, with a focus on high-frequency data for improved accuracy. The results of this study, which examine each option's characteristics or attributes, will add to the larger body of scholarly literature on the integration of cryptocurrencies into conventional financial markets. Moreover, drawing conclusions about some effects or prospective connections between cryptocurrencies and the financial industry based on their similarity to the American stock market. To pave the way for better-informed financial decision-making, this research aims to deepen our understanding of the interactions and spillover effects between cryptocurrency volatility and the American stock market.

Can the ‘good-bad’ volatility and the leverage effect improve the prediction of cryptocurrency volatility?—Evidence from SHARV-MGJR model

In recent years, cryptocurrencies have gained investor attention for their extreme volatility, but this has introduced financial risks that require accurate prediction models. Therefore, we propose the SHARV-MGJR model, which incorporates both ‘good-bad’ volatility, leverage effects, and current return information to enhance the accuracy of cryptocurrency market volatility predictions. Empirical results demonstrate that compared to GARCH-type models, the SHARV-MGJR model exhibits superior predictive accuracy in forecasting cryptocurrency market volatility. Furthermore, robustness tests confirm the superiority of the SHARV-MGJR model in predicting cryptocurrency market volatility.

One crash, too many: Global uncertainty, sentiment factors and cryptocurrency market

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.

Correlations versus noise in the NFT market.

The non-fungible token (NFT) market emerges as a recent trading innovation leveraging blockchain technology, mirroring the dynamics of the cryptocurrency market. The current study is based on the capitalization changes and transaction volumes across a large number of token collections on the Ethereum platform. In order to deepen the understanding of the market dynamics, the inter-collection dependencies are examined by using the multivariate formalism of detrended correlation coefficient and correlation matrix. It appears that correlation strength is lower here than that observed in previously studied markets. Consequently, the eigenvalue spectra of the correlation matrix more closely follow the Marchenko-Pastur distribution, still, some departures indicating the existence of correlations remain. The comparison of results obtained from the correlation matrix built from the Pearson coefficients and, independently, from the detrended cross-correlation coefficients suggests that the global correlations in the NFT market arise from higher frequency fluctuations. Corresponding minimal spanning trees for capitalization variability exhibit a scale-free character while, for the number of transactions, they are somewhat more decentralized.

DEVELOPMENT OF A CRYPTOCURRENCY TRADING STRATEGY USING MACHINE LEARNING METHODS

This article presents the results of a study aimed at forecasting signals for buying and selling Bitcoin cryptocurrency using machine learning models. The conducted analysis included the study of cryptocurrency features and markets, technical analysis, development of trading strategies, application of mathematical methods based on moving averages, and building classification models for buy or sell signals. The results demonstrate the effectiveness of applying machine learning models in modern trading strategies in the cryptocurrency market.

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.

Candlestick Pattern Recognition in Cryptocurrency Price Time-Series Data Using Rule-Based Data Analysis Methods

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.

The trend of digital finance: unveiling the multidimensional network of cryptocurrency risk propagation

ABSTRACT This study primarily explores the mechanisms of risk propagation among cryptocurrencies, unveiling for the first time the frequency dimension of risk propagation within the cryptocurrency market and identifying the role of oscillation frequency in this process. By employing Variational Mode Decomposition (VMD) and the DY spillover matrix to construct a complex network, the paper analyzes the frequency dimension risk propagation mechanisms of nine major cryptocurrencies from 2017 to 2023. Key findings include the significant risk propagation capabilities of Ethereum (ETH) and Bitcoin (BTC) during periods of high market volatility, while stablecoins such as Tether and USD Coin exhibit minimal risk propagation ability. Additionally, the characteristics of cryptocurrency risk propagation have been enhanced following the COVID-19 pandemic. Overall, the risk propagation of most cryptocurrencies is primarily realized through high-frequency oscillations. The robustness of the conclusions is verified using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. The results are significant for understanding the dynamic characteristics of the cryptocurrency market, predicting future market risks, and formulating risk management strategies. Furthermore, the methodology and findings of this study provide new perspectives and tools for exploring the risk propagation relationships among cryptocurrencies.

Международные тренды цифровизации валютно-финансовых отношений: от глобализации к децентрализации

The need for the use of new, digital forms of organization of monetary and financial settlements with foreign partners, as well as the formation of a national system of secure payments. The article presents the result of analysis of motivations of introduction of digital currencies of central banks (positive and negative moments of the considered process). The article gives an overview of existing international payment systems, including the Russian one, and identifies advantages and disadvantages. The main tendencies of the cryptocurrency market as a possible mechanism of formation of decentralized finance are considered.

Әртүрлі оқиғалардың криптовалюта құнына әсерін талдау

The relevance of the research topic is due to the fact that many crypto investors are attracted by the high liquidity of cryptocurrencies, low transaction costs and ease of transactions via the Internet. In contrast from Fiat currencies, corporate stocks, and bonds, cryptocurrencies do not have an underlying value. demand therefore market cost to a greater extent depends on how wellknown and popular this cryptocurrency is. Also, the price of cryptocurrencies is influenced by market sentiment, namely the thoughts, feelings and emotions of investors regarding the asset. With the help of traditional asset valuation models, it is impossible to qualitatively explain the latest changes in the price of cryptocurrencies. However, some financial models point to that cryptocurrency is currently overvalued. Apparently, the hypothesis of financial instability is better than any of the verified economic theories suitable to explain recent changesin prices of cryptoassets. The theory suggests that because the cryptocurrency market is moving against the macroeconomic fundamentals of the economy, emotions are a major factor. determining demand on a given market. In this article, the authors examined the influence of behavioral financial factors on investment decisions in the cryptocurrency market. Multiple regression analysis was used to examine this effect. The purpose of the article is to study the development of cryptocurrency in Kazakhstan and assess the impact of events on the value of cryptocurrency. Based on this goal, the authors put forward the following tasks: to analyze and evaluate the use of cryptocurrencies in the modern economy; explore the features of cryptocurrency pricing.

The asymmetric effects of upside and downside risks in cryptocurrency markets: Insights from the LUNA and FTX crises

Unlike conventional studies on the risk dynamics of cryptocurrency markets, this paper innovatively distinguishes between good and bad volatility to more accurately measure the market's risk spillover under both good and bad shocks. By constructing a network of cryptocurrency market risk spillovers, we delve into the asymmetry between good and bad volatility in the context of risk spillover. Specifically, this study focuses on the risk spillover characteristics during the LUNA and the FTX crises, aiming to uncover the unique manifestations of market risk during this tumultuous period. We find that, overall, the risk spillover from bad volatility tends to exceed that from good volatility. However, at higher levels of risk spillover, the spillovers from good volatility significantly exceed those from bad ones. This observation substantiates the notion that spillovers from good volatility are more likely to accumulate risks, potentially triggering a crisis. This research not only aids investors in better understanding the risk structure of the cryptocurrency market but also provides regulatory bodies with a reference for formulating risk control strategies, thereby holding significant implications for the healthy development of the cryptocurrency market.