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.

Asset allocation has always been a challenging issue / for individuals and businesses to survive in our competitive world. One of the famous businesses, which has an enormous impact on people's lives worldwide, is the pension industry. Pension funds- as Defined Benefit, Defined Contribution, or others- accept reserves from contributors and try to invest them in a way to keep up with their obligations in the future or even pay more than that. The equity market has been one of the good choices for investment as pension funds try to reach a particular rate of return to maximize their wealth while considering not crossing red lines in taking risks. This paper will detail the new mathematical model for finding optimal stock portfolios using Generalized Co-Lower Partial Moment as a risk measure to minimize portfolio optimization. On the other hand, it introduces new tailored Expected Utility as a performance metric to maximize in this model. The proposed model's issue against previous studies is considering risk aversion and target rate of investment return as two significant investor characteristics. This is based on price returns' simulation of candidate stocks in TSE while using accurate and nonparametric Probability Density Function in historical data analysis.