Cryptocurrency Trading Research Articles

Quantitative Finance and Information Technologies: A Comparative Analysis of Quantitative Trading and Cryptocurrency and Their Regulatory Challenges

As technology has improved in the last decade, financial institutions have developed new technologies, including quantitative trading and cryptocurrency, to enhance their financial products and services. This paper first provides a brief background of quantitative trading and argues for the transactional efficiency of quantitative trading over traditional trading practices; it characterizes quantitative trading as fast and precise. Meanwhile, the study also accounts for the regulatory concerns–including data leakage and platform security–that quantitative trading firms may encounter. This study then establishes a distinction between cryptocurrency and quantitative trading–the former is money-driven, and the latter is data-driven. This paper then discusses the speculative nature of cryptocurrency and addresses its financial concerns citing the FTX collapse. Overall, this paper establishes the argument that quantitative trading supported by technological experts and facilitators offers more advantages than disadvantages compared to cryptocurrency trading. This research concludes that since quantitative trading and cryptocurrency trading are conducted without consideration for international boundaries, they offer bold financial potential as alternatives to traditional banking practices, as long as specific international financial laws are complied with.

Engagement in New Gambling Practices and its Association with Gambling Disorder, Impulsivity, and Cognitive Distortions: Findings from a Nationally Representative Sample of Spanish Gamblers.

Most epidemiological surveys focus on adult gambling behaviors related to traditional gambling forms, while studies on novel forms often focus on loot boxes and cryptocurrency trading individually. This study examines the co-ocurrence of emergent gambling and gambling-like practices, analyzing the demographic and psychological characteristics of involved gamblers. A cross-sectional study surveyed 1429 Spanish individuals aged 18-65, using a web-based questionnaire. The survey assessed participation in 19 gambling (e.g., lotteries, sports betting) and gambling-like activities (e.g., trading of cryptocurrencies and other assets, buying loot-boxes), along with sociodemographic and substance use. Problem gambling (PGSI), Impulsivity (UPPS-P), and cognitive distortions (Labrador's cognitive distortions scale) were also assessed. Participants who gambled over the past year (n = 921) were classified into four groups: traditional gambling (TG) only (64.5%, n = 594), TG with trading activities (27.5%, n = 253), TG with gambling withing video games or streaming platforms (2.5%, n = 23), and TG with both trading and video gambling (5.5%, n = 51). Most gamblers engaged exclusively in traditional formats, but 35.5% also participated in novel gambling forms. Those involved in both trading and video gambling were generally younger, male, with higher levels of impulsivity and gambling-related cognitive distortions compared to TG-only gamblers (p < 0.001). This group also exhibited higher rates of problem gambling and substance use (p < 0.001). This study emphasize the importance of including emerging gambling activities, which are particularly prevalent among high-risk gamblers, in epidemiological surveys. Identifying new gambling patterns and associated risk factors could help optimize public policies and develop more effective regulatory and prevention strategies.

Uncovering the Bitcoin investment behavior: An emerging market study

Bitcoin remains a popular investment choice despite the regulatory obstacles and failures of many crypto firms. This intriguing behavior of investors necessitates calls for more in-depth research. This study explores the underlying motivations behind the intention to invest in Bitcoin by considering inaction regret aversion, overconfidence bias, herding, risk affinity, profit expectancy, perceived ease of investing, and social media influence in shaping the investors’ attitude towards investing in Bitcoin and consequently on behavioral intention to invest in Bitcoin. The study employs PLS-SEM and mediation analysis on a sample of 439 individuals from India with no history of cryptocurrency trading or investment. Path analysis demonstrates that inaction regret aversion, risk affinity, profit expectancy of Bitcoin, perceived ease of investing in Bitcoin, and social media influence are significant positive predictors of attitude toward investing in Bitcoin. Notably, profit expectancy remains the most relevant variable in the stated context. Attitude toward investing in Bitcoin positively and significantly influences the behavioral intention to invest in Bitcoin. The current study also indicates the significance of attitude as a mediator in the mentioned context.

Bayesian machine learning framework for characterizing structural dependency, dynamics, and volatility of cryptocurrency market using potential field theory

Identifying the structural dependence between the cryptocurrencies and predicting market trend are fundamental for effective portfolio management in cryptocurrency trading. In this paper, we present a unified Bayesian machine learning framework based on potential field theory and Gaussian Process to characterize the structural dependency of various cryptocurrencies, using historic price information. The following are our significant contributions: (i) Proposed a novel model for cryptocurrency price movements as a trajectory of a dynamical system governed by a time-varying non-linear potential field. (ii) Developed a Bayesian machine learning framework for inferring the non-linear potential function from observed cryptocurrency prices. (iii) Proposed that attractors and repellers inferred from the potential field are reliable cryptocurrency market indicators, surpassing existing attributes in the literature. (iv) Analysis of cryptocurrency market during various Bitcoin crash durations shows that attractors captured the market trend, volatility, and correlation. In addition, attractors aids explainability and visualization. (v) The proposed cryptocurrency market indicators (attractors and repellers) was used to improve the prediction performance of state-of-art deep learning price prediction models.

Online probabilistic knowledge distillation on cryptocurrency trading using Deep Reinforcement Learning

Leveraging Deep Reinforcement Learning (DRL) for training agents for financial trading has gained significant attention in recent years. However, training these agents in noisy financial environments remains challenging and unstable, significantly impacting their performance as trading agents, as the recent literature has also showcased. This paper introduces a novel distillation method for DRL agents, aiming to improve the training stability of DRL agents. The proposed method transfers knowledge from a teacher ensemble to a student model, incorporating both the action probability distribution knowledge from the output layer, as well as the knowledge from the intermediate layers of the teacher’s network. Furthermore, the proposed method also works in an online fashion, allowing for eliminating the separate teacher training process typically involved in many DRL distillation pipelines, simplifying the distillation process. The proposed method is extensively evaluated on a large-scale cryptocurrency trading setup, demonstrating its ability to both lead to significant improvements in trading accuracy and obtained profit, as well as increase the stability of the training process.

Deep Reinforcement Learning in Cryptocurrency Trading: A Profitable Approach

This study proposes an Automatic Cryptocurrency Trading System using Deep Reinforcement Learning (DRL). Six popular cryptocurrencies were used: Bitcoin, Ethereum, BinanceCoin, DogeCoin, Cardano, and WAVES. Development of the trading system started with building three timeseries models – Temporal Convolutional Neural Network (TCNN), Long Short-Term Memory Network (LSTM), and Gated Recurrent Unit Network (GRU) – to predict future prices. Then, cryptocurrency sentiment data was scraped using the Alternative.me API. Data on historical prices, predicted future prices, cryptocurrency sentiment index, technical indicators, and trading account information was fed as input states to three DRL Agents — Deep Q Network (DQN), Advantage Actor Critic (A2C), and Recurrent Proximal Policy Optimization (RPPO) — which were trained using a custom-developed trading environment. Each agent was given $1000 initial capital for all six cryptocurrencies to trade using three possible actions — Buy, Sell and Hold — and were back-tested on one year of unseen data. Our DQN model had the highest overall return on investment (ROI) of $740, an average 12.3% ROI across all six cryptocurrencies, with an ROI of 63.98% achieved for BinanceCoin. However, A2C and RPPO both had negative ROI.

Bitcoin: How Can it Disrupt Traditional Finance and Redefine Economic Landscape in Indonesia

Cryptocurrency has been a trending topic in the last few years. Many people especially from Gen Z are currently talking and investing in Cryptocurrency. In the most simple term Cryptocurrency is a digital currency. The pioneer of all Cryptocurrency is called Bitcoin. Bitcoin has been around for quite some time, founded by an anonymous guy called Satoshi Nakamoto in 2008. The main problem from Satoshi's perspective is that the world uses a centralized financial system. To counteract this problem Satoshi made Bitcoin have a system that is decentralized. First of all, this research will discuss and explain how and can Cryptocurrency disrupt the current traditional financial system often known as banking. This research will also focus on the euphoria of Cryptocurrency trading and investing. How does Cryptocurrency affect the way people invest nowadays and the impact on the financial economy market. The last is that this research is going to explain the prospect for Cryptocurrency in the future and can it replace the current fiat money in the world. This study will conduct qualitative research and the result of this research is used for understanding the basic concept of Bitcoin and Cryptocurrency.

Heuristic Bias and Herding Behavior for Predicting Investor Decision in Cryptocurrency Trading

Heuristic Bias and Herding Behavior for Predicting Investor Decision in Cryptocurrency Trading

Cryptocurrency Trading, Day Trading, and Gambling Behavior: Examining the Moderating Effects of Financially Focused Self-Concept and Gambling Motives.

Cryptocurrency and day trading have grown in popularity over the past decade following the creation of the first cryptocurrency, Bitcoin, in 2009. These activities share important features with gambling, including risking money on an uncertain outcome, a chance of monetary rewards, and the potential to experience harm (e.g., financial or relationship problems). However, little is known about cryptocurrency and day trading engagement in the adult population, including associations with gambling behavior, harm, and psychological factors that might moderate these relationships. We analyzed cross-sectional data for n = 822 adults from an online panel in the U.S. to examine: (1) the extent to which cryptocurrency trading, day trading, and gambling are associated, (2) relationships between cryptocurrency trading, day trading, and higher risk gambling behavior, and (3) whether financially focused self-concept and four types of gambling motives moderate these relationships. We found moderate to strong positive intercorrelations between cryptocurrency and day trading, and gambling behavior, including engagement and risk. We identified significant moderating effects of financially focused self-concept, and coping motives for gambling, on the relationship between cryptocurrency trading and gambling frequency, and between day trading and gambling frequency. For the models predicting higher risk gambling status, the only significant moderators were financially focused self-concept for the day trading model, and the enhancement motive for the cryptocurrency and day trading models. Our results have important implications for understanding interrelationships between gambling-adjacent activities and more traditional gambling forms, as well as the moderating roles of key psychosocial concepts in these relationships.

Sentiment Analysis of User Reviews on Cryptocurrency Application: Evaluating the Impact of Dataset Split Scenarios Using Multinomial Naive Bayes

The surge in cryptocurrency investors in Indonesia, reaching 18.83 million by January 2024, signifies an expanding interest in this market. This research conducts a sentiment analysis of user reviews on Indodax and Tokocrypto, the premier cryptocurrency trading platforms in Indonesia. Utilizing the Multinomial Naive Bayes method, the study examines the influence of various dataset split scenarios and random states on the model's performance. The findings reveal substantial variability in the model's accuracy based on different random states and test sizes. Notably, the Positive sentiment label consistently shows high-performance metrics, while the Neutral label underperforms. These insights are invaluable for developers aiming to improve user experience and for investors seeking to make informed decisions. This research underscores the significance of sentiment analysis in understanding user interactions and enhancing the credibility of cryptocurrency investment platforms.

ANALYSIS OF THE IMPACT OF CRYPTO TRADING ON CARBON EMISSIONS USING PATH ANALYSIS

In parallel with the growing interest in cryptocurrencies, the relationship between crypto trading and CO2 is critical to drive financial markets and environmental sustainability efforts. The aim of this study is to analyse the impact of crypto trading on carbon emissions (CO2) through the mediating roles of international trade and energy use. Within the scope of the study, path analysis was carried out using the 2007-2021 period data of the top 20 countries with high crypto trade volume. A model proposal was presented to examine the relationships between the variables used in the study and the necessary analyses were carried out. The results of the analysis show that crypto trade volume has a positive and significant effect on carbon emissions. It is also concluded that international trade and energy use mediate the relationship between crypto trade and carbon emissions. It is seen that crypto mining activities and cryptocurrencies lead to an increase in energy use and environmental impacts. At this point, it is important for countries to switch to renewable energy sources and to regularly report and monitor the impact of cryptocurrency mining activities on carbon emissions.

Green gold or carbon beast? Assessing the environmental implications of cryptocurrency trading on clean water management and carbon emission SDGs

This study addresses the ongoing debate concerning the environmental implications of cryptocurrencies. Specifically, it investigates the impact of Bitcoin trading volume on water and sanitation (Sustainable Development Goal (SDG) 6) and climate action (SDG 13). The research employs Ordinary Least Squares (OLS) panel data analysis to examine these relationships using a sample of 32 countries with available Bitcoin trading volume data from 2013 to 2020. The findings indicate that Bitcoin trading significantly and positively impacts progress towards SDG 6, suggesting potential benefits for water and sanitation initiatives. However, the study reveals a significant negative impact of higher Bitcoin trading volume on increased carbon emissions, underscoring the environmental costs associated with cryptocurrency activities. Similar impacts are observed for gold reserves, as their mining necessitates substantial energy consumption. These results highlight the need to regulate cryptocurrency trading and promote voluntary sustainable practices, particularly given the disparities between developed and emerging markets based on their governance frameworks. Additionally, the study considers the disparities between countries based on technology exports and economic policy uncertainty as influential determinants. The study's results emphasize the importance of proactive measures to ensure the responsible and sustainable use of cryptocurrencies. While cryptocurrencies offer significant economic returns, their early adoption stage necessitates further investigation into environmentally friendly approaches. Potential strategies include directing financial returns from cryptocurrencies towards alternative energy projects and supporting other environmental SDGs, thereby fostering a positive impact on the overall ecosystem. The study's implications extend to policymakers, regulators, and stakeholders, advocating for comprehensive and collaborative efforts to integrate sustainability into the rapidly evolving cryptocurrency market. This integration is crucial to ensure that the economic benefits of cryptocurrencies do not come at the cost of our environment.

Studies on Moral Judgment and Cognition Involving Cryptocurrencies and Tax Evasion

Although new digital monetary instruments (e.g., cryptocurrencies) have emerged and proliferated, there is little academic research concerning consumer perspectives on these instruments or the effects of these instruments on consumers’ moral judgments. The present research addressed this gap in the literature by addressing the following research question: Is the morality of tax evasion judged differently when tax evasion is undertaken with cryptocurrency vs. traditional financial instruments (e.g., stocks)? In two online experiments, we exposed participants to a tax evasion scenario with either cryptocurrency or stock trading. The results of Study 1 showed that tax evasion is perceived as being less wrong when it is carried out with cryptocurrency trading rather than stock trading. Study 2 replicated Study 1 and tested an explanatory mediating mechanism hypothesized to underlie the main effect. A mediation analysis showed that the effect of cryptocurrency (vs. stock trading) on the perceived wrongness of tax evasion is partially mediated by the observer's positive (vs. negative) affective evaluation of the person committing tax evasion. This paper also discusses the theoretical, practical, and societal implications of the present results, including the likelihood that people's attitudes toward moral transgressions and crimes will be more lenient when these acts are conducted with new, innovative, and exciting instruments.

Cryptocurrencies Price Estimation Using Deep Learning Hybride Model of LSTM-GRU

One of the financial assets in currency exchange is now cryptocurrency. The public is drawn to cryptocurrency trading because it is considered a lucrative form of investing. For cryptocurrency investors to maximize their earnings, accurate price forecasting is crucial. As price forecasting involves time series analysis, a hybrid deep learning model is suggested to project cryptocurrency prices in the future. Long Short-Term Memory and Gated Recurrent Unit (LSTM-GRU) networks are integrated into the hybrid model. Three cryptocurrency datasets are evaluated using the suggested hybrid model: Ethereum, Ripple, and Bitcoin. According to experimental results, the suggested LSTM-GRU model may provide the lowest MSE and RMSE values on the Bitcoin dataset (0.0611 and 0.2472), the Ethereum dataset (0.0369 and 0.19222), and the Ripple dataset (0.0006 and 0.0247).

Enhancing digital cryptocurrency trading price prediction with an attention-based convolutional and recurrent neural network approach: The case of Ethereum

To predict Ethereum price fluctuations, this study proposes a new two-stage Machine Learning approach using an improved convolutional neural network and a recurrent neural network framework, integrating an attention mechanism-based distribution function algorithm. We construct a dataset and perform model training, fitting, and forecasting. The results indicate that compared with traditional neural networks and time-series models such as GRU and ARIMA, respectively, this approach can effectively use the data information of digital cryptocurrency and improve the prediction accuracy and interpretability of attention-based allocation functions. This study contributes to the literature by offering a new approach for stakeholders.

FEATURES OF USING VBA IN TEACHING ACTUARIAL MATHEMATICS

The article is devoted to the analysis of practice of application of specific computational techniques within MS Excel, including VBA, for teaching selected topics in actuarial mathematics. Formulation of the problem. With the evolution of financial and actuarial models towards complexity and reliance on machine learning and data science, there is a growing demand for modern mathematicians, particularly actuaries, to acquire new skills and knowledge. This demand is being driven by new fintech trends in banking and insurance, such as smart payments, real-time credit risk assessment, automated reputation management, customer analytics, fraud detection and cryptocurrency trading. As a result, the training of future actuaries must meet high standards to ensure they are equipped to handle the sophisticated mathematical calculations required by insurance companies and other financial institutions. Materials and methods. To yield results, both theoretical methods, which involve analyzing books and publications, in particular in professional journals in the domains of finance and actuarial mathematics, and empirical methods, entailing the observation of the educational process aimed at training prospective actuaries, are employed in this study. Results. A study aimed at tackling the challenges encountered in teaching actuarial mathematics topics, such as life expectancy and mortality tables, revealed that incorporating a balanced blend of theoretical concepts and hands-on applications, coupled with illustrating mathematical models through real-world examples and reinforcing diverse computational techniques, constitutes a cornerstone of successful training for aspiring actuaries. Leveraging a versatile and user-friendly tool like VBA (Visual Basic for Applications) integrated into MS Excel enables educators to customize examples and assignments to match the intricacy and emphasis of their syllabus, thereby offering students a more targeted learning journey. Conclusions. The teaching of mathematical disciplines, especially financial and actuarial mathematics, in academic programmes for future actuaries requires the adaptation of traditional methods and approaches. In particular, the integration of VBA is key to teaching specific actuarial mathematics concepts, which aims to equip students with the necessary practical competencies. VBA facilitates interactive learning by enabling students to manipulate variables and observe changes in calculations in real time, which improves their understanding of actuarial concepts. VBA also allows you to automate repetitive tasks involving complex mathematical calculations, which saves time and reduces errors. Therefore, its use is recommended when teaching topics involving tabular data, especially life expectancy.

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 democratization of digital currency in Nigeria: A sentiment analysis of eNaira app usability

Globally, Central Bank Digital Currency (CBDC) is receiving a lot of attention in digital currency deliberations since it is at the top of the agenda of almost every central bank today. Although there is a growing body of literature on CBDCs, user perspectives on their usage and adoption in the literature are very sparse. This study set out to analyze factors impacting the actual usage of CBDC through sentiment analysis of eNaira, the first CBDC to be issued in Africa by the Central Bank of Nigeria (CBN). The research relies on the 214 data points extracted from eNaira App users’ comments from Google Play and App Store from 2020 to 2021. It framed its analysis based on theories of moral sentiments and appraisal theory of emotion. This research shows that eNaira users faced multiple challenges; however, CBN's responses to these challenges are minimal. The results highlighted the need to include public sentiments in policy formulation; the Nigerian government's ban on cryptocurrency trading negatively impacted the uptake of the eNaira due to mistrust. This research study contributes theoretically to appraisal theory and the theory of moral sentiment by explaining the evolution and public sentiments towards adopting the novel eNaira in Nigeria.

The Rise of Bitcoin ETFs and its Impact on Existing Crypto Currency Exchanges

The emergence of Bitcoin Exchange-Traded Funds (ETFs) marks a significant milestone in the evolution of cryptocurrency investment. This research paper investigates the potential impact of Bitcoin ETFs on existing cryptocurrency exchanges, focusing on liquidity dynamics and institutional investment trends. By examining the mechanisms through which ETFs may draw liquidity away from direct Bitcoin exchanges, this study aims to shed light on the evolving landscape of cryptocurrency trading. Additionally, the paper explores the potential for Bitcoin ETFs to attract increased institutional investment in the cryptocurrency market, analyzing the factors that may contribute to this trend. Through a comprehensive analysis of market data and expert insights, this research provides valuable insights into the evolving relationship between Bitcoin ETFs and traditional cryptocurrency exchanges.

A computational approach to cryptocurrency marketing on social media

This study aims to explore social media content associated with cryptocurrency marketing. We employ unsupervised Latent Dirichlet allocation topic modeling and sentiment analysis techniques to 98,716 tweets to examine Twitter (now known as X) content for subjects and sentiments related to cryptocurrency. Our findings reveal that cryptocurrency tweets fell into four categories, with ‘cryptocurrency trading,’ ‘NFT airdrop,’ ‘cryptocurrency affiliate program,’ and ‘Dogecoin on social media’ being the most popular. Furthermore, most of these topics exhibited positive sentiments. This study contributes theoretically by integrating cryptocurrency marketing into the diffusion of innovation paradigm. In addition, it offers strategic insights for digital marketers in identifying prevalent topics and sentiments related to cryptocurrency, enabling the tailoring of affiliate marketing communication strategies on social media.