فهرست مطالب mojtaba dastoori

Exchange rate fluctuations are always one of the variables affecting economic activities and thereby affecting the behavior of actors in capital markets. Therefore, the study of these relationships is of particular importance. On the other hand, the present study is conducted to study the effect of exchange rate fluctuations’ spillover on capital market indicators of selected oil-exporting OPEC member countries. These countries are similar in terms of economic reliance on oil resources, but different in terms of economic growth rate and capital market characteristics. The research period is from 2016 to 2021 and data related to exchange rates and capital market indicators of Iran, Iraq, UAE, Qatar, Saudi Arabia, Oman and Bahrain are collected from reliable sources and using multivariate GARCH models. Findings from the experimental data show that currency fluctuations in the capital market affect the capital market index of Iran, Iraq, UAE, Qatar, Saudi Arabia, Oman, and Bahrain. This effect is asymmetric only in the Iranian capital market, and is symmetrical in other countries.

Lack of liquidity management of banks is one of the most important risks for any bank and if less attention is paid to liquidity risk, it may lead to irreparable consequences; Preventing liquidity risk requires a comprehensive measurement method but liquidity risk is complicated issue, and this complexity makes it difficult to provide a proper definition. In addition, defining liquidity risk determinants and formulation of the related objective function to measurement its value is a difficult task. To address these problems, in this study we propose a model that uses artificial neural networks and Bayesian networks. Design and implementation of this model includes several algorithms and experiments to validate the proposed model. In this paper, we have used Lunberg-Marquardt and Genetic optimization algorithms to teach artificial neural networks. We have also implemented a case study in Bank Mellat to demonstrate the feasibility, efficiency, accuracy and flexibility of the research liquidity risk measurement model.

The current research has been done pursuing the goal to present a content marketing model with brand improving approach in banking industries. This study is applied in terms of goal and survey- exploratory concerning the approach. The statistical community is made up of a group of experts including the senior executives of the banking industries, academic faculty in marketing and marketing consultants acquainted with the banking industries who were posed in-depth interview. Selecting the experts and their interviews continued until reaching theoretical saturation and then stopped. In this research, snowball sampling method was used and this process continued until the researcher reaching theoretical saturation. And ultimately, the study finished with interviewing 9 experts. Since the data-driven theory has been utilized in this research, collecting the data has been mainly done by in-depth and unstructured interview. Finally, after three steps known as open, axial and selective coding, the conceptual model has been designed based on the paradigm model.

This study was conducted with the aim of designing and explaining a content marketing pattern with a brand reinforcement approach in the country's banking industry. This research is applied in terms of objective, exploratory in terms of approach, and mixed in terms of data analysis. In the qualitative phase of the research and in order to design a model based on methodology of data foundation theory, a group of experts including senior managers of the banking industry, university professors in the field of marketing, and marketing consultants familiar with the banking industry were considered as the statistical population. Snowball sampling method was used in this phase, and this process continued until reaching the theoretical saturation. 9 interviews were conducted in total. Also in quantitative phase, the customers of the banking industry in the city of Tehran were considered as population and 450 people were selected among them as the statistical sample based on equal size cluster sampling. In the qualitative phase of the research, due to using the data foundation theory, the main data collection tool was unstructured in-depth interviews with experts. In the quantitative phase of the research, the main data collection tool was a closed-ended researcher-made questionnaire consisting of 37 items that were designed based on the initial conceptual model. The SPSS, LISREL, and smart-PLS pieces of software were used to perform descriptive and inferential analyzes in the quantitative phase of the research. Finally, the research results led to designing a content marketing pattern with a brand reinforcement approach in the country's banking industry with 11 main variables and the hypothetical relationships of the model were tested and approved in a large population.

This article, had been analyzed the persistence in cryptocurrency market. At the beginning of the process, the autocorrelation of the time series data of four cryptocurrencies Bitcoin, Light Coin, Ripple and Ethereum during the period 2017 to 2020 has been studied. The data of this research are obtained daily from the investing database showed significant autocorrelation among cryptocurrencies. Long-term memory methods such as R/S analysis and fraction integration are used for analysis of market persistence. The results of this study showed that this market is persistence. That means there is a positive correlation between past and future values, and its level and value change over time. These results provide evidence for market inefficiency. Using the pair trading algorithm and redefining it, profit has been made and the results show a return of 1463% in 2 years from 2018 to 2020. The use of trading algorithms based on market memory and cointegration has the potential to generate profits, and the development of models and algorithms can help investors to generate returns and on the other hand lead to more efficient market in the long term.

In today's world, capital markets have increased the possibility to generate profits through high-frequency transactions due to the advancement of computer technology and the use of IT infrastructure. The main objective of this research is to examine and provide a model of paired trading algorithms that can generate higher returns than coherent algorithms. In the background study, one of the limitations of the paired trading algorithm is the use of constant limits and rules that cannot model the dynamics of the system. This research based on the purpose is applied research and in terms of collecting data in the form of descriptive research with the approach of presenting a model. In order to test the model, using the data of 1395, intraday stock prices was used. The statistical population of this study was the top 50 Tehran Stock Exchange companies, using filtering the stocks to create a pair of shares decreased to 33, these shares was selected as sample. The results of the study were presented by implementing two algorithms of paired trading algorithms and paired trading algorithms statistical process fuzzy control. Finally, the results showed that the modified algorithm during the similar period of the investment could produce
57.95% of the return, while the base model had 46.17% returns on investment