In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.

Cryptocurrencies are crucial in modern commerce and finance, whether at the national, corporate, or individual level. They serve as fundamental currencies for buying and selling, enabling various business transactions. However, the rise of cybercrime has brought about concerns regarding their operations, potential breaches in encrypted currencies, and the security systems managing them. The frequency of attack tactics and the motivation of attackers seeking financial gain are well-known. Many cryptocurrencies lack the necessary algorithms, techniques, and knowledge to effectively detect and mitigate malware, making them vulnerable targets for hackers. In this study, machine learning techniques are employed to detect malicious code in digital currencies. Additionally, a comparison of these techniques is conducted to determine the most suitable algorithm and technology, Furthermore, this study highlights the importance of effective malware detection in securing cryptocurrencies. Three datasets of different sizes were used, each yielding distinct results based on dataset size. The AdaBoost model demonstrated superior performance when applied to the short dataset, while the decision tree model performed best with the medium-sized dataset. Conversely, the Naive Bayes model consistently produced the worst results, while the large-size KNN model achieved the highest performance.

The purpose of the current paper is to identify influential aspects of published literature and future research questions to set forth future research agenda based on comprehensive literature review using bibliometric and content analysis. The study analyzed 1225 documents from the international Scopus database using bibliometric analysis and content analysis. VOSviewer software is used for bibliometric analysis. The analysis revealed that most of the information was derived from the Finance Research Letters. Moreover, the United Kingdom is the most cited country, while Tianjin University in China has the highest publications affiliations. Furthermore, the analysis shows that the keyword analysis of cryptocurrency literature had four classes of research streams in cryptocurrency, namely, cryptocurrency, Blockchain, Fintech, and currency, representing the most upcoming trends. The present study makes a significant contribution to the literature by providing a framework for future research. The framework provides opportunities to future researchers to explore the web of relations among some identified research streams as future research agenda.