درخت حوزه‌های تخصصی

The impact of personal judgment on the assessment of an individual’s financial situation has been drastically reduced through the development of credit scoring. The systems are capable of deciding based on an applicant’s total score which is a combination of several factors and indicators. Over the past few decades, credit scoring has been considered an essential tool for evaluation in various institutions and has also been able to transform the industry as a whole. Most of the research conducted in the field has taken into account traditional credit scoring, but considering the ever-evolving technological world that we live in and the increasing emergence of new social media networks, such research has now become obsolete. Such technological advancements have not only paved the way for far more sophisticated credit scoring systems but also essentially rendered the previous generations useless. It should be noted that credit scoring and its features have widely been discussed across the globe but, considering the various aspects and models that have to be taken into account, no one best method has been designed or suggested for it so far. This study shows that social media channels tend to perform relatively well in predicting stock market trends when the overall index is growing positively. The research also illustrates that a higher number of days of activity and a large number of signals released do not necessarily mean that the channels can or have credited their offered stock return on a one-month time frame. The methodology used is "CRISP-DM," which consists of six steps. The main variables include social and financial variables that are examined for six months. In the research, we seek to identify, analyze and categorize active telegram channels in stock signals using the data mining model and the RFM method. The k-means algorithm is selected for this category. Then, in each cluster, the importance of social variables and the performance of the channels are extracted by the EXTRATREECLASSIFIER algorithm, and channel performance is measured by considering the changes in the total index.

Nowadays, maritime trade plays an important role in the economy of some countries, including Iran. Especially, some of the ports in Iran that are located on the Persian Gulf border carry out container shipping operations. Therefore, in the present study, we investigate the impact of the most important macroeconomic factors on the container trading in Iranian ports in the Persian Gulf. For this purpose, we employed the autoregressive distributive lag (ARDL) approach for the estimating effect of oil price, gross national production, industrialization and exchange rate on the container trading volume. The used data are quarterly. Container business data is related to the ports of Shahid Rajaei, Bushehr, Imam Khomeini, Khorramshahr, Shahid Bahonar, which are obtained from the Ports and Maritime Organization. The results of this study show a negative impact of increasing oil price on Iran's container trading. Moreover, our empirical findings indicate that the effect of industrial indicator (INDS) on the container trading volume is positively. Also, one percent increases in gross national production (GNP) leads to %0.37 increase  in container trading, and one percent increase in exchange rate (EXH)  leads to %.17 decrease  in container trading, respectively. Finally, coefficient of ECM is -0.54. It means that speed of adjustment in the function of container trading volume is relatively high, and in each period, 54 percent deviation from long run direction of container trading volume to be corrected by variables of model.