درخت حوزه‌های تخصصی

The present study aimed to investigate the impact of supply chain innovation on performance through the mediating role of strategic agility. The statistical population of this research consists of all employees and managers of the food industry in Tehran. Necessary data were collected with a standard questionnaire from 70 food industry managers who were selected by simple and easy non-probability sampling. The least squares method and SMART PLS software were used to analyze the model. The results of testing the hypotheses indicate the positive effect of supply chain innovation on firm performance and the positive effect of supply chain innovation on strategic agility was also confirmed. Strategic agility also had a positive effect on firm performance.

In this paper we distinguish between operational risks depending on whether the operational risk naturally arises in the context of model risk. As the pricing model exposes itself to operational errors whenever it updates and improves its investment model and other related parameters. In this case, it is no longer optimal to implement the best model. Generally, an option is exercised in a jump-diffusion model, if the stock price either exactly hits the early exercise boundary or the price jumps into the exercise price region. However paths of the diffusion process are continuous. In this paper the impact of operational risk on the option pricing through the implementation of Mitra’s model with jump diffusion model is presented. A partial integral differential equation is derived and the impact of parameters of Merton’s model on operational risk and option value by operational value at risk measure is employed. The option values in the presence of operational risk on data set are computed and some of the results are presented.

The Iranian economy in recent years is due to the development of economic sanctions, a sharp decline in the price of oil and the deficit resulting from revenue - dependent on oil revenues and the trend towards tax revenues. While comparing the volume of the Iranian economy with the amount of tax income indicates the existence of a relatively significant tax gap. The tax gap is the difference between the collected taxes and the tax required by the law. The purpose of this study is to answer the question whether corporate governance is effective and strong on the relation between tax gap and future earnings changes? The statistical population of the research in Tehran Stock Exchange (TSE) firms and statistical sample consists of 120 companies in the period of 2007-2017. In order to test the hypotheses, multivariate regression using mixed data - data approach has been used. The results indicate that there is a significant inverse relationship between the tax gap and future earnings changes. It can be argued that increasing the difference between earnings accounting earnings can be associated with decreasing interest in the next year and less stability. On the other hand, significant positive relation between corporate governance is efficient and strong with future earnings changes. because corporate governance will ultimately lead to more sustainable future gains due to the decline of discretionary accruals in discretionary accruals. It is also reinforced by the effect of the tax gap on future earnings changes in firms that have efficient corporate governance, and this effect is only seen for a year later. And is not effective for the second and third years.

In recent years, authors have focused on modeling and forecasting volatility in financial series it is crucial for the characterization of markets, portfolio optimization and asset valuation. One of the most used methods to forecast market volatility is the linear regression. Nonetheless, the errors in prediction using this approach are often quite high. Hence, continued research is conducted to improve forecasting models employing a variety of techniques. In this paper, we extend the field of expert systems, forecasting, and model by applying an Artificial Neural Network. ANN model is applied to forecast market volatility. The results show an overall improvement in forecasting using the neural network as compared to linear regression method.