Abstract: This study examines the relationship between short-term and long-term stock returns of companies after initial public offering by considering financial variables and financial and ownership characteristics of companies on the Tehran Stock Exchange. The research sample includes 4560 companies that were publicly listed on the stock exchange in the period from 2013 to 2024, which constitute a total of 4560 company-years. Econometric methods and vector regression models have been used to test the hypotheses. First, the statistical description of the data has been discussed and then various tests including ADF and PP unit root tests to examine the stationarity of the data, Durbin-Watson test to examine autocorrelation, Chow test, F test and Hausman test have been used to select the appropriate model. The results of these tests show that the main hypothesis of the study is that there is a significant relationship between short-term and long-term stock returns of companies after initial offering is confirmed. Finally, the results of this study can be generalized with 95% confidence to the entire statistical population of the study, namely active investors in the Tehran Stock Exchange.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)

Abstract: 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.

	© 2010 by the authors; licensee Growing Science, Canada. This is an open access article distributed under the terms and conditions of the license. Creative Commons Attribution (CC-BY)