محمدجواد زارع بهنمیری

The purpose of this study was to investigate the effect of peripheral uncertainty on the relationship between the herd behavior of investors and the price of negative stock shock. Because the capital market is one of the main pillars of the country's economic growth and development, the incidence of any disturbance and deviation in prices causes problems with allocating and equipping funds. Reducing stock prices is an example of the disorders created in the capital market. On the other hand, one of the factors that lead to the fluctuations of return and instability of financial markets is the herd behavior of investors. If investors lack sufficient information about environmental factors, environmental uncertainty occurs. Such a situation also affects the organization. Environmental uncertainty overshadowed the use of financial statements. Changes in stock prices and corporate status are cases that can not speak in full confidence in their occurrence. As a result, environmental uncertainty is part of the economic environment. To test the research hypotheses, 156 companies from the Tehran Stock Exchange during the years 2010 to 2020 were selected by screening method. To test the hypotheses, multivariate regression models were performed using STATA14 software. The results indicate that herd behavior has a significant effect on price shock and environmental uncertainty has no effect on the relationship between the two.

Delay is an event that increases the completion time of a part of the project, which is one of the main problems in the executive projects of the country and causes an increase in project costs as well as damages. Project delays pose significant risks to the project that are dangerous for the project to continue. These risks are of particular importance in oil and gas projects. The purpose of this study is to identify and rank the risks related with delays in oil and gas projects. The present study is applied in terms of orientation and quantitative in terms of methodology. The statistical population of the study is managers and experts of risk and delay in oil and gas projects in the country. Among these people,15 people were selected as the sample by judgmental sampling method. For data collection, two questionnaires of expertise and prioritization were used, both of which had validity and reliability. In the first step, the risks associated with project delays were extracted by reviewing project risk and delay articles. In the next step, these risks were screened using the Binominal test.11 risks had a significance coefficient higher than 5% and were excluded from the calculations. The remaining 8 risks were prioritized using the Codas distance technique. According to the data of the relative evaluation matrix and the scores of each risk, the risks of sanctions, inflation, and lawsuits and complaints had the highest priority, respectively. Finally, research proposals were developed based on significant risks.

In a volatile economic environment, financial decision making is always associated with risk. Bankruptcy, as one of the most important risks, has a significant impact on the interests of the firm's stakeholders, so presenting appropriate bankruptcy forecasting patterns is of the utmost importance. In this study, after reviewing the theoretical literature and selecting the financial ratios used in previous bankruptcy prediction models as the variable input of the initial model, the financial ratios were adjusted based on the price index and then, using the LARS algorithm, the ratios that have the highest ability to differentiate between bankrupt and non-bankrupt firms were identified, and finally, using the SVM and Naive Bayesian algorithms, the final bankruptcy prediction model was developed. For this purpose, the data of 50 companies listed in Tehran Stock Exchange who had experienced bankruptcy for at least one year from 2008 to 2018 under Article 141 of the Commercial Code were used. The results show that the adjusted financial ratios based on the price index in the model presented by SVM algorithm can be a very good predictor for bankruptcy of companies with an accuracy of 99.4%.

It seems that paying attention to social responsibility by companies can lead to a better stakeholder’s view toward the company, thereby increasing their loyalty and trust. Having the ability to obtain more financial resources in times of crisis, due to the greater loyalty of investors, will result in reducing the company risk. In contrast, being overconfident about the loyalty of individuals to the company can lead to keeping a short-term debt structure, thereby increasing the risk of obtaining financial resources. Recently, the negative impacts of petrochemical companies on the environment have made social and environmental groups focus more on this industry, and this focus has pushed companies into involving in more social activities. Considering the potential impact of CSR on the company’s risk, this study examines the relationship between corporate social responsibility and value at risk in petrochemical industry using a sample of 27 companies listed on the Tehran Stock Exchange during the 2010-2017 period. Eviews 10 is used for computing and analyzing the data, and the generalized auto regressive conditional heteroskedasticity (GARCH) model is employed to estimate value at risk. The results indicate a negative and statistically significant relationship between corporate social responsibilities and company value at risk. <strong> </strong>