جستجوی مقالات مرتبط با کلیدواژه "financial factors" در نشریات گروه "مالی"

In recent years, Bitcoin has garnered increasing public and media attention, as well as investment in this field. This cryptocurrency currently has the highest market value among existing virtual currencies that can operate in a decentralized manner based on defined incentives. These incentives include a fixed mining reward for each block and a variable reward resulting from transaction fees in the blockchain network. The transaction fee in the Bitcoin network is unstable and is determined in real-time. Due to the reduction of the fixed mining reward, the transaction fees have become the primary source of income for miners. This study examined the long-term and short-term effects of technical and financial factors on user behavior in determining Bitcoin transaction fees. The autoregressive distributed lag (ARDL) model was used for this analysis. The dataset spanned from April 10, 2018, to July 24, 2023. The technical factors examined included the average daily block size in bytes, network difficulty, and daily transaction volume. The financial factors included the average daily value of transactions sent to Bitcoin and the average daily Bitcoin price in dollars. The results showed that the technical factor of network difficulty had the greatest long-term impact on transaction processing fees. Additionally, the average daily block size in bytes had a significant long-term impact on transaction fees. However, the Bitcoin price did not have a significant long-term impact on transaction fees. These findings can help users make more informed decisions when setting transaction fees. Additionally, the results can assist miners in adopting better strategies to maximize their earnings.

The purpose of this article is to examine the effect of economic-financial factors, the effect of exchange rate shock and the effect of GDP shock on the country's Islamic contracts for the years 1363-1400. The research method is using SVAR vector autoregression model. This research is applied in terms of its purpose and descriptive-analytical in terms of its nature and method. The data of this study were obtained from official information publishing databases, such as the Central Bank and Central Bank's Monetary and Banking Research Institute, as well as statistical yearbooks. SVAR structural auto-regression econometric methods were used to analyze the collected data and Evioz 12 econometric software was used for statistical analysis. At first, the research model was specified, then reliability tests were performed using the Phillips-Peron method, and then the co-integration tests were performed using the Johansen-Jisilius test, and then the model was estimated using the SVAR method. And model stability tests, IMPULSE RESPONSE and variance analysis were also conducted and finally the effect of research variables was discussed. The results of this research show that there is a significant effect between the effect of exchange rate shock and the effect of GDP shock on the country's Islamic contracts.

In behavioral finance and economics’ discussions, financial worry and rumination are mentioned as two types of financial distress for investors. Although both worry and rumination are related to anxiety and other negative emotional status, but rumination focuses on bad feelings and past experiences, while worry focuses on the potential for bad events to happen in the future. Given the relationship between financial rumination and financial worry with people's financial status in the past, present, and future, in this research, was examined the relationship between financial background and demographic characteristics with these two variables. The statistical population is the investors of the Yazd Regional Stock Exchange, and due to the homogeneity of the population, a simple random sampling method was chosen using a questionnaire for 123 people as the sample size. The data collection and the confirmation of the relationship between variables and factors conducted through confirmatory factor analysis and PLS modeling technique. The conducted tests indicated the validity and reliability of the questionnaire. The results suggested that current and future financial status, and current financial and demographic factors have a significant effect on the perceptions and emotions of financial concern. Also, person’s background and demographic characteristics have a significant effect on the perceptions and emotions of financial rumination.

Many companies are using the environmental accounting system to find a suitable way to report and disclose information to the public. Evidence shows that the information provided by the green accounting system is useful for users and plays an important role in their decisions. Industrial, refineries and power plants and smelting and steel companies. These economic actors, their investors, and on a larger scale, society as a whole, will never achieve this success unless they are indifferent to the threats to human and environmental well-being in the long run. In such situations, governments, stock exchanges, markets, investors, professional legislators, academics and other community activists call on companies to be clear about their goals, performance and impact on sustainability. The main purpose of this study is to identify the financial factors affecting green performance in the steel industry. In order to achieve this important, the research was conducted during 1399 and 1400 in two separate stages.

In the research, Explain the role of financial and economic variables on stock returns With Markov-switching Model.  Based on information collected from the two codal.ir and TSE.ir websites, availability and momentum. contrarian strategies have been dealt with in the market. From among 318 companies from those listed in Tehran Stock Exchange for the period of 2011-2017 and based on restrictions, 108 companies have been selected as research samples.Then, using various statistical models, three statistical models of autoregressive time series (with no help from auxiliary variable), linear regression, and Markov-switching have been applied. Using the model’s goodness of fit criterion, these three models have been compared and the best one has been selected. Based on selected model, stock trading strategy for the next 12 months has been predicted. To collect data and perform statistical analyses, Excel spread sheet, as well as SPSS and R software packages have been used.According to the findings, from among minor variables (base volume, institutional investment, and free float) and major variables (currency and inflation rates), only three variables from minor types (base volume, institutional investment, and free float) have been effective on stock trading strategy; and, these variables can be used as auxiliary variables to predict return on stock and to specify stock trading strategy in future, as a result.

Today, one of the major problems of banks is the amount of their non-performing loans. The ratio of non-performing loans is used as a measure of bank health. Therefore, the purpose of this study is to investigate the factors affecting the ratio of non-performing loans. In this regard, the vector Auto Regressive– vector error correction model has been used to examine the effects of macroeconomic and financial variables such as business cycle, government debt, unemployment rate, foreign direct investment, and domestic credit by the banking sector. To achieve the research goal, quarterly data from 1996 to 2017 were collected from Iranian banks. According to the results of the study, the relationship between business cycle, government debt, and foreign direct investment and the ratio of non-performing loans is negative, while he relationship between domestic credit provided by the banking sector and non-performing loans ratio is positive. Unlike other variables, the unemployment rate variable has a short-term effect on the ratio of non-performing loans. According to the results of analysis of variance of the estimated model in the long run, approximately 48.96% of the changes in the ratio of non-performing loans, through the past values ​​of this variable, 41.42% by the government variable, 4.14% by the banking sector domestic credit variable, 3.12% by the unemployment rate variable. 1.3% is explained by the business cycle variable and 1.6% by the foreign direct investment variable.