درخت حوزه‌های تخصصی

The aim of the current research is to identify the futures of marketing in the banking industry with a focus on blockchain technology. The current research is applied in terms of direction and has a mixed methodology due to the use of qualitative and quantitative methods together. The theoretical population of the research was experts in banking marketing and digital financial technologies, especially blockchain, and the sampling method was done in a judgmental method. The sample size in this study was equal to 15 people. For data analysis, meta synthesis methods, Binominal's statistical test, developed Copras and root definitions tool were used. The tools of data collection in this research were interviews and questionnaires (expert evaluation and priority evaluation questionnaires). 47 drivers were extracted from meta synthesis and these drivers were classified into nine main drivers. The drivers of the research were screened in two stages using theory-based inference screening and Binominal's test, and 12 drivers were considered for prioritization using the Copras technique. The remaining drivers were evaluated using the Copras technique and three criteria of importance, degree of certainty and experts' expertise. The findings showed that the drivers of marketing researchers' interest in digital financial technologies and blockchain and the development of decentralized banking had the highest priority and were selected for scenario planning. Based on these two drivers, four scenarios of crypto bank, conservative banking, pioneer banking and traditional banking were developed. The research proposals were proposed based on the important drivers and the desired scenario (crypto bank).

This study examines trading strategies based on trading systems by analyzing the performance of 11 technical indicators. The data used for analysis were financial data of all firms listed on the Teh-ran Stock Exchange in the period from 2010 to 2020. Excluding the firms whose data were not available for the period under study, 135 firms were selected as the research sample. The results showed that the signals containing three indicators of moving average, exponential moving average, and relative strength over a weekly up to six-month period to buy or sell stocks (as a strategy) could be used more confidently compared to other indicators to achieve higher returns and profitability. As a result, investors can use the signals that these three indicators in weekly (EMA) and monthly (MA, RSI) periods and the quarterly (MA) and six-month (RSI, EMA) periods to determine buying and selling strategies with the lowest investment risks. It is also recommended that investors use a combination of these three indicators to invest, and extend their investment period over a longer period of time to bear less risk, and more returns.

Qualitative information security risk assessments are somewhat subjective and the high degree of subjectivity associated with the perception of risk means that management is often skeptical of risk analysis results, and is unwilling to make important decisions based on that. Besides, the process of information security risk assessment is quite complex and rife with uncertainty and without taken into account the uncertainty of information security risk assessment the results can be misleading. Therefore, in this paper, the Fuzzy Multi Criteria Group Decision Making (FMCGDM) model is proposed to address the above-mentioned problems. The focus group method used to identify risk parameters and the Delphi method is used to construct a hierarchy for risk parameters. The findings of this research would be useful for the information security department to become more capable in analyzing the InfoSec risks and reducing the consequences of subjective assessment. A case study involving an actual information security risk management project was presented to illustrate the use of the proposed model. Computational results demonstrated the efficiency and effectiveness of the presented model that can assist InfoSec risk analyst to better evaluate InfoSec risk.