Cryptocurrency Trading Research Articles

Blockchain metrics and indicators in cryptocurrency trading

The objective of this paper is the construction of new indicators that can be useful to operate in the cryptocurrency market. These indicators are based on public data obtained from the blockchain network, specifically from the nodes that make up Bitcoin mining. Therefore, our analysis is unique to that network. The results obtained with numerical simulations of algorithmic trading and prediction via statistical models and Machine Learning demonstrate the importance of variables such as the hash rate, the difficulty of mining or the cost per transaction when it comes to trade Bitcoin assets or predict the direction of price. Variables obtained from the blockchain network will be called here blockchain metrics. The corresponding indicators (inspired by the “Hash Ribbon”) perform well in locating buy signals. From our results, we conclude that such blockchain indicators allow obtaining information with a statistical advantage in the highly volatile cryptocurrency market.

Gambling-like digital assets and gambling severity: a correlational study with U.S. sports bettors consuming cryptocurrencies, NFTs, and fan tokens

ABSTRACT This paper explores the relationship with gambling severity of a relatively new set of speculative digital products, namely cryptocurrency trading, non-fungible tokens (NFTs), and fan tokens. These products have gained notoriety in sports marketing strategies (among other realms) and are considered to be potentially problematic because, disguised as non-gambling products, they include gambling-like design features that might make sports fans more vulnerable when consuming them. A sample of regular sports bettors (N = 525) based in the U.S. was recruited in 2023 via an online panel to understand the association between these types of consumptions. The findings revealed a significant overlap between experiencing gambling problems and consuming cryptocurrency trading products, NFTs, and fan tokens. The results indicate a modest relationship between the severity of gambling and the likelihood of owning the studied digital assets, of holding positive attitudes toward them, and of being more frequently exposed to their advertising and marketing promotions. The relevancy of these findings resides in the fact that sports bettors might be perceiving and treating gambling and gambling-like assets as part of the same product category, when in reality the latter are legally required to comply with fewer consumer protection measures than gambling products.

Development of recurrent neural networks for price forecasting at cryptocurrency exchanges

The study focuses on improving the quality of using recurrent neural networks (RNNs) to predict cryptocurrency prices. The formula of the target variable for the model based on the arithmetic mean is developed, which allows us to better take into account the dynamics of cryptocurrency exchanges. The factors affecting this variable were grouped into features based on the volume of daily cryptocurrency trading, the volatility of the relevant prices, and the pre-calculated and selected signals of technical indicators. As part of the study, an algorithm for processing daily data was developed for the model. The results obtained made it possible to create a holistic model for forecasting stock prices. Two recurrent neural networks were trained: one with a long short-term memory (LSTM) and the other with a recurrent gate unit (GRU). To determine the efficiency of the models, the analysis was carried out using two key indicators: the Sortino coefficient, which measures the relative risk/reward for each additional unit of unwanted volatility, and the Sharpe ratio, which measures the return on assets, subtracting the free risk. As a result, it was found that both models have similar results in terms of accuracy (~69 %). Still, the GRU-based model showed significantly better values of the Sortino coefficients (3.13) and Sharpe’s coefficient (2.45), which allows us to conclude that it is effective on cryptocurrency exchanges. At the same time, the LSTM model requires more parameters for training than the GRU model with an identical structure, which leads to a longer training time. The obtained scientific and practical results are aimed at more efficient use of recurrent neural networks in price forecasting on cryptocurrency exchanges

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

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

Electronic Finance and Cryptocurrency Market

In an age of rapidly changing technological revolutions, where cryptocurrencies and blockchain play key roles, studying the dynamics of cryptocurrency markets at the government level is becoming an urgent need, which is not just a step into the future, but also an opportunity for countries to act forward, based on data analysis and forecasting global economic trends. Every aspect of cryptocurrency - from financial stability to technological innovation - has the potential to transform the global landscape. Studying the interaction of cryptocurrencies with national interests will not only help to determine the positions of countries in this context, but also formulate effective strategies for managing this rapidly developing economic segment. It is important to realize that those states that integrate cryptocurrency market analysis into their strategies can best adapt to the challenges of the modern world and promote their economic prosperity.The purpose of the research is to study how the introduction of digital money into the economy affects the interest of various countries in participating in trading in the cryptocurrency market. To identify the relationship between the integration of such assets into the economy and the desire of host countries to participate in cryptocurrency markets. Consequently, there is a need to analyze the mechanisms of interaction of large economic entities - states - with cryptocurrencies, as well as predict the likely responses in this context of research. Using panel data analysis, to conduct a study of the dynamics of the cryptocurrency market in the digital finance market using the example of 50 countries around the world. To identify the relationship between the attitudes of countries and the dynamics of the cryptocurrency market in order to suggest possible directions for the future development of the studied evolutionary economic sphere.Materials and methods. As a basis for the study, a balanced and informative set of indexes (17 indexes) was identified, which represents the key variables necessary for a more in-depth analysis of the dynamics of cryptocurrency markets in the context of various countries over a period of ten years (2013-2022). The “Cryptocurrency trading volume” index was chosen as the effective index. The set of indexes was selected based on their ability to reflect cryptocurrency trading volumes, investor activity, and each country’s level of involvement in cryptocurrency transactions. The impact of various factors on the volume of transactions with electronic money and digital financial assets was assessed using panel data analysis methods in the Gretl statistical analysis program.Results. As a result of the analysis using the panel data tool, three models were created: a pooled regression model, a fixed-effects model, and a random-effects model. The choice of the best model is made through testing special hypotheses - the Brisch-Pagan test and the Hausman test. The fixed effects model was preferable to the random effects model in this study. The reason is the fixed effects model’s ability to take into account the individual characteristics of each country in the sample, leading to more accurate results. Based on the study of individual fixed effects, three groups of countries were identified: those that have a positive impact on the volume of cryptocurrency trading (for example, the United States and Japan), countries with a neutral impact (for example, Germany), and countries where individual effects have a negative impact (for example, China and Russia).Conclusion. Overall results indicate that countries with advanced digital infrastructure and ease of use of electronic payments, as well as inflationary and cultural influences, may exhibit higher activity in cryptocurrency markets. Based on the fixed effects model and taking into account assumptions about the dynamics in different countries, general conclusions were formulated regarding the index analyzed in this study - the volume of cryptocurrency trading.

Digital Transformation Management of Supply Chain Firms Based on Big Data from DeFi Social Media Profiles

Emerging technologies in the digital context can favor industrial sector firms in their aim to improve their performance. Digitalization is mainly expressed through the utilization of big data that originate from various sources. Blockchain technology has led to the extended adoption of capitalization of Decentralized Finance (DeFi) services, such as cryptocurrency trade platforms. Supply chain firms, in their quest to exploit any means and collaborations available to promote their services, could place advertisements on DeFi’s social media profiles to boost their financial performance. Social media analytics, as a part of the big data family, are an emerging tool for promoting a firm’s digital transformation, based on the plethora of customer behavioral data they provide. This study aims to examine whether the social media analytics of DeFi platforms are capable of affecting their website visibility, as well as the financial performance of supply chain firms. To do so, the authors collected data from the social media profiles of the most-known DeFi platforms and web analytics from the most significant supply chain firms’ websites. For this purpose, proper statistical analysis, Fuzzy Cognitive Mapping, Hybrid Modeling, and Cognitive Neuromarketing models were adopted. Throughout the present research, it has been discerned that from an increase in the social media analytics of DeFi platforms, their website visibility increases, while the organic and paid traffic costs of supply chain firms decrease. Supply chain firms’ website customers tend to increase at the same time.

Evaluating the Mediating Mechanism of Perceived Trust and Risk toward Cryptocurrency: An Empirical Research

In recent years, investing in cryptocurrencies has become a fad worldwide. However, fewer studies have explored the intention of using cryptocurrencies. Different from traditional financial commodities, cryptocurrencies have a unique appeal. Therefore, this study uses technology mindfulness, technology readiness, and financial literacy, and through perceived trust and perceived risk, to understand users’ intentions to use cryptocurrencies. The respondents to this study were cryptocurrency owners or those having financial investment experience. A total of 337 valid samples were obtained and analyzed by the partial least square-structural equation model (PLS-SEM) method. The results showed that technology mindfulness and negative technology readiness significantly impacted perceived trust and perceived risk. On the other hand, perceived trust and perceived risk significantly impacted the intention to use. Finally, this study proposes theoretical and practical implications which can help cryptocurrency trading platforms understand the motivations of potential and existing users. Finally, this study can also serve as a reference for cryptocurrency trading service providers to develop marketing strategies.

Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis

This research explores the impact of social media sentiments on predicting Bitcoin prices using machine learning models, integrating on-chain data, and applying a Multi Modal Fusion Model. Historical crypto market, on-chain, and Twitter data from 2014 to 2022 were used to train models including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting, and Multi Modal Fusion. Performance was compared with and without Twitter sentiment data which was analysed using the Twitter-roBERTa and VADAR models. Inclusion of sentiment data enhanced model performance, with Twitter-roBERTa-based models achieving an average accuracy score of 0.81. The best performing model was an optimised Multi Modal Fusion model using Twitter-roBERTa, with an accuracy score of 0.90. This research underscores the value of integrating social media sentiment analysis and onchain data in financial forecasting, providing a robust tool for informed decision-making in cryptocurrency trading.

Blockchain and Freedom to Conduct a Business: Between Myth and Reality

Recent advances in technology have demonstrated the enormous flexibility of Distributed Ledger Technology (DLT), whose potential goes well beyond the cryptocurrency trading. This article explores the potential impact that the utilization of a permissioned blockchain could have on listed companies and how this could be an appropriate instrument for a more effective implementation of the provisions of the Shareholder Rights Directive II. This technological infrastructure could attest the correct formation of will within the shareholders’ meeting and contribute to the creation of a truly democratic space for the meeting and discussion among shareholders, thus allowing achievement of freedom to conduct a business. However, the most advanced use of blockchain technology has a disruptive effect and exposes us to the great danger of an “algocratic” drift. Therefore, this article analyses the main critical issues from both a technical and legal viewpoint of Decentralized Autonomous Organizations (DAOs).

A broader perspective on cryptocurrency trading: consumer-driven value, online communities and heuristics are drivers for consumer behaviour

PurposeThis review aims to provide an overview of research from different academic disciplines to chart some of the key developments in retail cryptocurrency trading against the backdrop of the wider trading landscape, and how it has evolved in recent years. The purpose of this review is to provide researchers with a broad perspective to highlight the complex range of factors that drive cryptocurrency trading among retail investors.Design/methodology/approachPeer-reviewed literature from the social sciences, economics, marketing and branding disciplines is synthesised to explicate influential factors among retail cryptocurrency investors.FindingsOnline retail trading communities can create narratives that ascribe value to cryptocurrencies leading to consumer herding behaviours. The principles that underpin emotional branding and Fear of Missing Out can promote trading behaviour driven by heuristic processing and cognitive biases. Concurrently, the tenets of controversial marketing and the anti-establishment nature of Bitcoin and other cryptocurrencies serve to bolster in-group out-group categorisations fostering continued investment and market volatility. Consequently, Bitcoin and cryptocurrency trading more broadly offer a powerful combination of excitement from risk-taking akin to gambling buffered by the sanctity of social inclusion.Originality/valueA broader, unique perspective on retail cryptocurrency trading which assists in better understanding the complexities that underpin its appeal to retail investors.

Automated cryptocurrency trading approach using ensemble deep reinforcement learning: Learn to understand candlesticks

Despite their high risk, cryptocurrencies have gained popularity as viable trading options. Cryptocurrencies are digital assets that experience significant fluctuations in a market operating 24 h a day. Recently, considerable attention has been paid to developing trading bots using machine-learning-based artificial intelligence. Previous studies have employed machine learning techniques to predict financial market trends or make trading decisions, primarily using numerical data extracted from candlesticks. However, these data often overlook the temporal and spatial information of candlesticks, leading to a limited understanding of their significance. In this study, we utilize multi-resolution candlestick images containing temporal and spatial information. Our rationale for using visual information from candlestick charts is to replicate the decision-making processes of human trading experts. To achieve this, we employ deep reinforcement learning algorithms to generate trading signals based on a state vector that includes embedded candlestick-chart images. The trading signal is generated using a multi-agent weighted voting ensemble approach. We test the proposed approach on two BTC/USDT datasets under both bullish and bearish market scenarios. Additionally, we use an attention-based technique to identify significant areas in the candlestick images targeted by the proposed approach. Our findings demonstrate that models using candlestick images 'as-is', outperform those using raw numeric data and other baseline models.

Reconfigure and evaluate consumer satisfaction for Open API in advancing FinTech

With the rise of FinTech, Open API (application programming interface) is essential in advancing FinTech applications in the future, but its customer satisfaction is a concern. Therefore, this study uses the technology acceptance model (TAM) to reconfigure and evaluate customer satisfaction of Open API in advancing FinTech by applying extended variables. This study initially applies the decision making trial and evaluation laboratory (DEMATEL) approach with the analytic network process (ANP) technique to reconfigure the modified TAM. Finally, we use Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) analysis to evaluate customer satisfaction for examining the acceptance of Open API.The results of DANP point out ‘perceived privacy’ (0.179), ‘customer use intention’ (0.158) and ‘perceived usefulness’ (0.147) are the three most influential variables for Open API in advancing FinTech. Furthermore, VIKOR analysis indicates online security or cryptocurrency trading (0.536) performs best in which Open API can satisfy customers in advancing FinTech and online banking (0.438) has the most space for improvement. This study also indicates that privacy protection plays a very important role in consumer satisfaction of Open API. The influences of the variables demonstrate that Open API compared to conventional APIs, is more convenient and relatively beneficial in advancing FinTech.

Accounting and Tax Specifics Regarding Cryptocurrency Trading at National and European Level

Accounting and Tax Specifics Regarding Cryptocurrency Trading at National and European Level

Return-volatility relationships in cryptocurrency markets: Evidence from asymmetric quantiles and non-linear ARDL approach

Implied volatility has consistently demonstrated its reliability as a superior estimator of the expected short-term volatility of underlying assets. In this study, we employ the newly constructed robust model-free implied volatility (MFIV) indices for Bitcoin and Ethereum (BitVol and EthVol) to explore the asymmetric return-volatility relationship of these cryptocurrencies through the lens of behavioral finance theories. Utilizing the asymmetric quantile regression model (QRM) and the Non-linear ARDL (NARDL) approach, our results reveal a notable difference from equities. Both positive and negative return shocks in the cryptocurrency market lead to an increase in volatility. However, during high volatility regimes, positive (negative) return shocks exert a more substantial impact on positive innovations of volatility for Bitcoin (Ethereum) compared to negative (positive) return shocks. The degree of asymmetry steadily intensifies as we progress from medium to uppermost quantiles of the volatility distribution. These observed phenomena can be attributed to behavioral aspects among market participants, including noise trading, behavioral biases, and fear of missing out (FOMO). Our findings hold significant implications for various aspects of cryptocurrency trading, portfolio hedging strategies, volatility derivatives pricing, and risk management.

A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning

The popularity of cryptocurrencies has skyrocketed in recent years, with blockchain technologies enabling the development of new digital assets. However, along with their advantages, such as lower transaction costs, increased security, and transactional transparency, cryptocurrencies have also become susceptible to various forms of market manipulation. The pump and dump (P&D) scheme is of significant concern among these manipulation tactics. Despite the growing awareness of P&D activities in cryptocurrency markets, a comprehensive survey is needed to explore the detection methods. This paper aims to fill this gap by reviewing the literature on P&D detection in the cryptocurrency world. This survey provides valuable insights into detecting and classifying P&D schemes in the cryptocurrency market by analyzing the selected studies, including their definitions and the taxonomies of P&D schemes, the methodologies employed, their strengths and weaknesses, and the proposed solutions. Presented here are insights that can guide future research in this field and offer practical approaches to combating P&D manipulations in cryptocurrency trading.

Comprehending the Crypto-Curious: How Investors and Inexperienced Potential Investors Perceive and Practice Cryptocurrency Trading

With the increasing popularity of cryptocurrency, many people are interested in cryptocurrency investments, but have so far hesitated. Many others have made investments without adequate preparation. To help interested investors improve their understanding of cryptocurrency and make rational investment decisions, it is important to study their concerns and motivations and to draw upon experienced investors’ experiences and practices. Therefore, we surveyed crypto investors and inexperienced potential investors interested in trading cryptocurrency (n = 395). Our results showed that extreme price volatility is the primary incentive and a substantial obstacle to market participation. Fraud risks, lack of personal funds, insufficient knowledge, and difficulty identifying credible information sources are also common barriers. Our findings highlight the need to build trustworthy exchange platforms and integrate educational features. Based on the reported concerns and experiences, we (1) identify learning components for new investors, and (2) formulate design recommendations for beginner-friendly exchange platforms.

Cryptocurrency Trading and Downside Risk

Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies.

Developing an Ethical Framework for Responsible Artificial Intelligence (AI) and Machine Learning (ML) Applications in Cryptocurrency Trading: A Consequentialism Ethics Analysis

The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an extensive review of relevant literature, this study explores the ethical implications of AI and ML in the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading. The author proposes a typology framework for AI and ML trading by comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency trading. By applying a consequentialist lens, this study underscores the significance of balancing AI and ML’s transformative potential with ethical considerations to ensure market integrity, investor protection, and overall well-being in cryptocurrency trading.

Modeling Supply Chain Firms’ Stock Prices in the Fertilizer Industry through Innovative Cryptocurrency Market Big Data

Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step. Potential investors in supply chain firms in the fertilizer industry need to know whether the observation of data originating from the cryptocurrency market is capable of explaining their stock price variation. The authors identify the innovative utilization of cryptocurrency markets’ user analytical data to model and predict the stock price of supply chain firms in the fertilizer industry stock price. The main aim of this research is to evaluate the contribution of cryptocurrency market big data as a predicting factor for the stock price of fertilizer market firms. Such a finding improves the knowledge and decision-making of potential investors in the fertilizer market. Moreover, this study seeks to highlight the benefits of utilizing cryptocurrency market big data for other financial purposes, apart from stock price prediction. The analytical data was derived from cryptocurrency websites and applications and was then processed through statistical analysis (correlation and linear regressions), Fuzzy Cognitive Maps (FCM), and Hybrid Modeling (HM) modeling. The hybrid model’s simulation showed that analytical data from the cryptocurrency markets tend to explain and predict the stock price of supply chain firms in the fertilizer industry. Such data refer to Bitcoin’s website organic keywords and traffic costs, as well as paid traffic costs from cryptocurrency trade websites/apps. A rise in Bitcoin and cryptocurrency trade websites’ organic and paid traffic costs tend to increase supply chain firms in the fertilizer industry’s stock prices, while Bitcoin’s website organic keywords variation decreases accordingly.

Kripto Paraların Kumar ve Bağımlılık ile İlişkisi

Cryptocurrencies has been considered as both an investment tool and a great invention that will replace money and change the world order. Although crypto currency trading has been investigated in many aspects, the psychological dimension that directly affects investors has often been ignored. Control of cryptocurrency trading is in the hands of investors rather than a central authority or institution. Thus, the value of cryptocurrencies changes with the reactions of investors. This situation suggests that psychological factors may be more prominent in cryptocurrency trading. Cryptocurrency trading has many similarities with gambling and betting, such as risk taking, getting quick returns, extreme gains or losses. Some significant components of behavioral addiction are also seen in individuals who spend so much time with cryptocurrency trading. The purpose of this article is to provide a better understanding of the psychological effects of cryptocurrency trading, which has entered our lives over a relatively brief period of time and reached millions of investors.