آرش خلیلی نصر

The capital market plays a crucial role within a country's financial structure and is instrumental in funding significant, long-term projects. Investments in the railway transport industry are vital for boosting other economic areas and have a profound impact on macroeconomic dynamics. Nonetheless, the potential for delayed or uncertain returns may deter investors. Accurate predictions of rail company stock prices on exchanges are therefore vital for making informed investment choices and securing sustained investment. This study employs deep learning techniques to forecast the closing prices of MAPNA and Toucaril shares on the Tehran Stock Exchange. It utilizes deep neural networks, specifically One-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM) networks, and a combined CNN-LSTM model, for stock price prediction. The effectiveness of these models is measured using various metrics, including MAE, MSE, RMSE, MAPE, and R2. Findings indicate that deep learning methods can predict stock prices effectively, with the CNN-LSTM model outperforming others in this research. According to the results, The CNN-LSTM model reached the highest R2 of 0.992. Also, based on criteria such as MAE, MSE, RMSE, and MAPE the best results belong to LSTM (Kaggle-modified) with 521.715, 651119.194, 806.920, and 0.028, respectively.

foreign investments have always been welcome and policy makers always do their best in order to attract more and more capital into their area; But a question which gave rise to a series of studies is that is FDI always beneficial for the recipient and does it under all circumstances help the growth in the host economy? In order to answer this question, we first examined whether or not FDI, by itself, has any significant impact on growth and the results proved that FDI affects growth positively in our full sample. We then show that FDI’s effect on growth is different in developed and non-developed countries. A surprising finding in our study is that in developed countries foreign flows of investment do not affect economic growth where this effect in non-developed countries is relatively high and significant. Three different stock market indicators (market capitalization, value traded and turnover ratio) are then introduced and it is tested whether the differences in FDI’s impact in developed and non-developed countries is due to their stock-market-related financial absorptive capacities. Our key contribution in this paper, along with our other novel findings, is that we introduce cut-off levels for these three indicators which successfully split our sample into one sub-sample in which FDI strongly affects growth and one in which FDI’s effect on growth diminishes.     

This study aims to investigate the effect of perceived value of banking services on the key indicators of consumer behavior. The study population includes the customers of private banks in Tehran City. The data collection tool used in this study is a questionnaire the face validity of which was confirmed by the experts and respondents and the reliability of which was measured and confirmed using Cronbach's alpha coefficient and construct reliability (CR). Depending on the type of the sampling method which was non-random convenience sampling technique, a questionnaire was distributed among 300 customers of banks. Out of them, 236 questionnaires were returned and analyzed using partial least squares method, which is one of the methods of structural equation modeling. SPSS20 and Smart-PLS2.0 were used for this purpose. The results showed that the perceived value of banking services has a positive effect on consumer behavior and its key indicators such as satisfaction, loyalty and word of mouth advertising. Based on the findings of this study, it can be said that the results will lead to a better understanding of the mechanisms of consumer behavior, which is an acceptable basis for maintaining and increasing customer loyalty to banks.