An appraisal of ethical issues and the effect of artificial intelligence on the cryptocurrency market

Ogunyolu, Olufunmilola Adunni; Adebayo, Adewale O

doi:10.30574/gjeta.2022.11.2.0083

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Research has shown that some of the common features of fraudulent schemes are mouth-watering returns and no negative impacts 1 , 29 . Findings suggest that increased regulatory supervision with tight requirements for compulsory reporting of their activities will help to identify such schemes 30 .…”

Section: Related Workmentioning

confidence: 99%

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Onu,

Omolara,

Alawida

et al. 2023

Sci Rep

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Security threats posed by Ponzi schemes present a considerably higher risk compared to many other online crimes. These fraudulent online businesses, including Ponzi schemes, have witnessed rapid growth and emerged as major threats in societies like Nigeria, particularly due to the high poverty rate. Many individuals have fallen victim to these scams, resulting in significant financial losses. Despite efforts to detect Ponzi schemes using various methods, including machine learning (ML), current techniques still face challenges, such as deficient datasets, reliance on transaction records, and limited accuracy. To address the negative impact of Ponzi schemes, this paper proposes a novel approach focusing on detecting Ponzi schemes on Ethereum using ML algorithms like random forest (RF), neural network (NN), and K-nearest neighbor (KNN). Over 20,000 datasets related to Ethereum transaction networks were gathered from Kaggle and preprocessed for training the ML models. After evaluating and comparing the three models, RF demonstrated the best performance with an accuracy of 0.94, a class-score of 0.8833, and an overall-score of 0.96667. Comparative evaluations with previous models indicate that our model achieves high accuracy. Moreover, this innovative work successfully detects key fraud features within the Ponzi scheme dataset, reducing the number of features from 70 to only 10 while maintaining a high level of accuracy. The main strength of this proposed method lies in its ability to detect clever Ponzi schemes from their inception, offering valuable insights to combat these financial threats effectively.

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Section: Related Workmentioning

confidence: 99%

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Onu,

Omolara,

Alawida

et al. 2023

Sci Rep

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show abstract

“…These technological innovations have enabled faster execution of trades, increased market transparency, and facilitated the development of sophisticated trading strategies that respond to market signals and trends (Mikhaylov, 2020). However, they have also introduced new challenges related to market manipulation and cybersecurity (Ogunyolu & Adebayo, 2022).…”

Section: Sonal Sahumentioning

confidence: 99%

Eficiencia del mercado y anomalías de calendario pos-COVID: perspectivas de bitcoin y ethereum

Sahu

2024

TAJ

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Este estudio investiga los efectos del día de la semana en el mercado digital, con un enfoque en bitcoin y ethereum, abarcando desde el 1º de julio de 2020 hasta el 31 de diciembre de 2023, en el período posterior al COVID-19. Empleando pruebas paramétricas y no paramétricas junto con el modelo GARCH (1,1), se analizó la dinámica del mercado. Los hallazgos indican un efecto significativo del día de la semana en ethereum, caracterizado por notables variaciones de rendimiento entre diferentes días, mientras que itcoin no muestra anomalías de calendario discernibles, lo que sugiere una mayor eficiencia del mercado. La susceptibilidad de ethereum a estos efectos subraya las complejidades actuales del mercado. Las disparidades en las anomalías del calendario surgen de la evolución de la dinámica del mercado, las diferencias metodológicas y la naturaleza especulativa del comercio de criptomonedas. Además, el mercado descentralizado y global complica la identificación precisa de los efectos en todo el mercado. Este estudio proporciona evidencia empírica sobre los efectos del día de la semana en el mercado de criptomonedas, lo que facilita a los inversionistas refinar las estrategias comerciales y la gestión de riesgos. Se justifica realizar más investigaciones para explorar los mecanismos subyacentes y monitorear los desarrollos regulatorios y tecnológicos para obtener información de los inversionistas.

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An appraisal of ethical issues and the effect of artificial intelligence on the cryptocurrency market

Cited by 2 publications

References 12 publications

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Detection of Ponzi scheme on Ethereum using machine learning algorithms

Eficiencia del mercado y anomalías de calendario pos-COVID: perspectivas de bitcoin y ethereum

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