Correlation between capital markets and cryptocurrency: impact of the coronavirus

Ariya, Kanyawut; Chanaim, Somsak; Dawod, Ahmad Yahya

doi:10.11591/ijece.v13i6.pp6637-6645

Cited by 2 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a similar vein, Figure 3 displays the average mean between received transactions, with mean values plotted on the x-and density-y-axes. ERC-20 tokens have played a pivotal role in various blockchain-based applications by providing a standardized and interoperable way to represent and exchange digital assets on the Ethereum network [18], [19]. Regression analysis evaluates the overall fit of the model and estimates the regression coefficients of each factor in the equation by using statistical techniques.…”

Section: Proposed Modelmentioning

confidence: 99%

Cryptocurrency fraud detection through classification techniques

Tripathy,

Kumar Balabantaray,

Parida

et al. 2024

IJECE

View full text Add to dashboard Cite

Ethereum and its native cryptocurrency, Ether, have played a worthy attention in the development of the blockchain and cryptocurrency space. Its programmability and smart contract capabilities have made it a foundational platform for decentralized applications and innovations across various industries. Because of its anonymous and decentralized structure, the hotheaded expansion of cryptocurrencies in the payment space has created both enormous potential and concerns related to cybercrime, including money laundering, financing terrorism, illegal and dangerous services. As more financial institutions attempt to integrate cryptocurrencies into their networks, there is an increasing need to create a more transparent network that can withstand these kinds of attacks. In this work, we are using different classification techniques, such as logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) for Ethereum fraud detection. The dataset we are using includes rows of legitimate transactions done using the cryptocurrency Ethereum as well as known fraudulent transactions. The “XGBoost” model, which is noteworthy, detects variations that might attract notice and prevent potential issues in this chore.

show abstract

Section: Proposed Modelmentioning

confidence: 99%

Cryptocurrency fraud detection through classification techniques

Tripathy,

Kumar Balabantaray,

Parida

et al. 2024

IJECE

View full text Add to dashboard Cite

show abstract

Dynamic evolution of causal relationships among cryptocurrencies: an analysis via Bayesian networks

Amirzadeh,

Thiruvady,

Nazari

et al. 2024

Knowl Inf Syst

View full text Add to dashboard Cite

Understanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.

show abstract

Correlation between capital markets and cryptocurrency: impact of the coronavirus

Cited by 2 publications

References 26 publications

Cryptocurrency fraud detection through classification techniques

Cryptocurrency fraud detection through classification techniques

Dynamic evolution of causal relationships among cryptocurrencies: an analysis via Bayesian networks

Contact Info

Product

Resources

About