درخت حوزه‌های تخصصی

According to the study of Fang and his colleagues in 2021, in order to reduce the fall of the stock market, companies pay attention to trading strategies and behavioral financial perspective, which have evaluated the assumptions and goals set in this regard; But there may be a gap in this path that prevents the connection or effect between the above variables, which is discussed in this study. This study aims to determine the effect of quantitative trading strategies on the fall of the stock market according to the perspective of behavioral finance (case study: companies admitted to the Tehran Stock Exchange). In order to analyze the research hypotheses, the statistical population was selected from 2016 to 2021 and for 6 years and included listed companies; the statistical sample was selected by elimination method and 90 companies. The research method used is a descriptive-analytical method and historical research data was collected and classified by referring to the financial statements and financial reports of the companies admitted to the Tehran Stock Exchange. The panel data method was used to analyze the statistical data; And in the analysis section, descriptive statistics, inferential statistics and various relevant tests were performed and data analysis was done using Eviews version 9 software. According to the analysis of the regression model, the findings of the research show that the main hypothesis has been confirmed and it was found that quantitative trading strategies have a negative and significant effect on the fall of the stock market according to the behavioral financial perspective of the companies admitted to the Tehran Stock Exchange. So that in this research, based on the t-statistics, the effectiveness of both special hypotheses (fall-timing and movement-reverse) was determined, and according to the mediating variable of behavioral financial perspective, they can also play a positive and significant role in reducing the fall of the stock market to be.

The main purpose of the present study is to identify and investigate the factors affecting the revisit intention of a tourist destination and to prioritize them based on their level of importance. There are many factors that influence the revisit intention of a place, however, not many studies have been conducted to address the issue of identifying and prioritizing them. Accordingly, this study seeks to fill this gap. This study identified more than 200 factors through a comprehensive literature review and expert’s opinions and classified them into 17 main factors. These identified factors were then provided to tourism experts and they were then rated, using the Delphi method. Ultimately, 3 factors were removed and 14 factors remained as factors affecting the revisit intention. The prioritization of these factors and their importance was established using the Multi-Criteria Decision Method, i.e. the Best-worst method (BWM). The results showed that the behavior and culture of local people in a tourist destination is the most important factor for tourists in leading them to revisit that destination. The perceived value factor was ranked second and the motivation factor was ranked third, in this respect. Moreover, the factor of Word of Mouth (WOM) was the least important factor and therefore was ranked last. The findings of this study are useful in helping tourism marketing and branding strategists to focus on the right metrics to make more effective decisions and encourage people to revisit tourist destinations.

Scientific sources demonstrate different attitudes of researchers to cryptocurrencies because they treat them as a category of currency, virtual money, commodity, etc. Accordingly, the relation to the valuation and risk of cryptocurrency as an investment object is different. The purpose of the article is to identify cryptocurrency value formation factors and determine the risks of investing in cryptocurrency. Cryptocurrency is simultaneously considered a currency, an asset with uncertain income, and a specific product, the price of which is determined by the energy costs for mining new cryptocurrency blocks. Thus, the paper examines the risks of investing in cryptocurrency from several positions. First, the study identifies the factors of formation of the value and risk of cryptocurrency as ordinary money based on comparing cryptocurrency with traditional money. Unlike traditional money, cryptocurrency is not tied to the economic performance of a particular country; also, central banks do not control or regulate their mining. Instead, the cryptocurrency emissions depend on the computational capacity of the equipment used for their mining. As a financial asset, cryptocurrency can be a “financial bubble” because their value increasing often exceeds the cost of mining. On the other hand, given the emergence of cryptocurrency as a phenomenon of the information economy, the paper analyses the impact of specific technical features (cryptographic hashing algorithm, the complexity of creating new blocks, the technology of verification of mining operations, etc.) on the risk of investing in cryptocurrency assets.