درخت حوزه‌های تخصصی

Audit quality is one of the most important issues in the field of auditing and the capital market. Defining a framework for determining the quality of audit is important so the present study seeks to rank and compare the factors affecting audit quality from the perspective of different groups using a multi-attribute TOPSIS technique. In this research, first, 15 influencing factors were selected as quality indicators using library studies, and then these indicators were analyzed using TOPSIS multi-attribute technique by distributing questionnaires among four groups including: researchers and faculty members, audit institutes, chartered accountants, members of the audit committee, and financial managers of the companies listed on the Tehran Stock Exchange and they were rated and compared. From the view point of the of researchers and faculty members, auditing institutes, auditing committee members and chief financial managers, the most important effective factors on the audit quality are professional level of the auditor in the industry expertise, number of employed chartered public accountants and quality control score, respectively. The results of index comparison test indicates that from the view point of the four studied groups, there is a sensible difference between 15 abovementioned indicators.

One important issue in data envelopment analysis (DEA) which has been studied by many researchers is returns to scale (RTS). The authors developed DEA models to evaluate the efficiency of two-stage networks in returns to scale variable and introduced a new definition to determine return to scale classification in two-stage networks. The current article proposed an approach for determining the stability region of returns to scale classification in two-stage network DEA. The data were collected from insurance companies in Iran in 2019. We consider the insurance industry process as a two-stage network; the stage of marketing and that of investment. The effectiveness of insurance companies was evaluated, and, after determining the classification of returns to scale, we found a sustainability interval to classify returns to their scale.

The development of knowledge and advancement in technology has created a wide-ranging transformation in societies and has led to changes in the per-formance of companies in order to respond to environmental turmoil and social expectations. In this way, they tried to gain a significant competitive advantage over other competitors. The purpose of this study was to investi-gate the moderating role of managerial fortification in relation to the impact of technology-based capabilities on the product market competition of knowledge-based companies in the capital market: a source-based capabili-ties (DEA). This research is applied in terms of purpose and in terms of data envelopment analysis is a quasi-experimental and post-event research in the field of positive accounting research. Then, based on multivariate regression, the research hypothesis was tested using Eviews software. The statistical population studied in this study consisted of knowledge-based companies listed on the Tehran Stock Exchange during the years 2015 to 2019 and 79 companies were selected and reviewed based on systematic screening. The result of testing the research hypothesis is that the technological capabilities have a positive and significant effect on the product market competition of knowledge-based companies in the capital market. This result demonstrates that the resource-based technology capability based on the source-based capabilities is seen as a resource against stagnation in a competitive environ-ment and helps the company, to maintain flexibility in the face of environ-mental change in order to respond faster, at the same time to develop the company's competitive capacity to create new resources or new and innova-tive products, and to make the company's future more attractive to stake-holders with greater returns and potential risk control.

In the real world, situations frequently occur when we want to allocate a fixed cost between a set of decision-making units (DMUs) such as institutions, organizations. In this paper, we use the data envelopment analysis (DEA) technique to allocate fixed costs among DMUs. First, we introduce semi-additive production technology in DEA and present efficiency evaluation models in this technology. In estimating the frontier of this technology, in addition to the observed DMUs, the set of all aggregations of these DMUs are also used. In the following, we propose an interactive process for fixed cost allocation between DMUs in DEA based on the concept of cross-efficiency. We show that our proposed iterative approach is always feasible, and ensures that all DMUs become efficient after the fixed cost is allocated as an additional input measure. The cross-efficiency scores corresponding to all DMUs are improved at each stage of the interactive process. We also illustrate the proposed approach with a numerical example. The proposed approaches are demonstrated using an application of the fixed cost allocation problem for branches of commercial banks. Finally, we bring the results of the research.

The existence of bubbles in the market, especially the capital market, can be a factor in preventing the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. On the other hand, due to the goal of investors in achieving a portfolio of high returns with the least amount of risk, the need to pay attention to these markets increases. In this research, with the aim of maximizing return and minimizing investment risk, an attempt has been made to form an optimal portfolio in conditions where the capital market has a price bubble. According to the purpose, the research is of applied type, and in terms of data, quantitative and post-event, and in terms of type of analysis, it is of descriptive-correlation type. In order to identify the months with bubbles in the period from 2015 to 2021 in the Tehran Stock Exchange market, sequence tests and skewness and kurtosis tests were used. After identifying periods with bubbles, the meta-heuristic algorithms were used to optimize the portfolio. The results indicate the identification of 14 periods with price bubbles in the period under study. Also, in portfolio optimization, selected stock portfolios with maximum returns and minimum risk are formed. This research will be a guide for investors in identifying bubble courses and how to form an optimal portfolio in these conditions.

The purpose of this research is to investigate the prediction of corporate social responsibility reporting using financial ratios. To answer the research question, four prediction models of linear regression, K Nearest Neighbor, decision tree, and deep learning were investigated. Also, 61 financial ratios were used according to previous research using data related to listed and non-listed companies of Iran from the years 2012 to 2018. According to the re-sults obtained from the estimation of each of the proposed prediction mod-els, it can be stated that the k-nearest neighbor model has the lowest RMSE value, and in fact, this model predicts the amount of social responsibility with less error than other models. The linear regression model with the high-est RMSE value has a weaker performance than other models. LSTM model and decision tree respectively had the lowest RMSE value after the k-nearest neighbor model. As a result, since the LSTM model requires a large number of test sam-ples for deeper learning, it could not achieve high performance in the evaluated data set. Based on the investigations, it can be stated that the current research does not have a similar example inside or outside of Iran.

One of the goals of researchers and policymakers is to find measures to achieve economic growth. Financial development is one of the policies that many economists recommend in order to achieve economic growth and development. From this perspective, financial development is an engine for economic growth, and policymakers should focus on creating and expanding financial institutions and markets. The present study examines the impact of financial development and economic performance indicators including economic growth and international trade in developing and developed countries in the long run from 2001 to 2018. Data collection has been done by two methods, library, and field, to complete the literature and research background, refer to libraries and researches, and for financial and economic data, including financial development indicators in two sections: Bank- Index and Capital Markets Stock-Index, as well as figures for Gross Domestic Product (GDP) and international trade from the World Development Index (WDI) databases, are used. Developed countries, due to their technology and power in production, can carry out their industrial production and export to developing countries. However, developing countries do not see long-term equilibrium relationships for economic growth and international trade.

The bankruptcy of corporations causes huge losses for investors, managers, creditors, employees, suppliers, and customers. If someone understands the reason for the corporate's bankruptcy, then he can save the corporate from certain death with the necessary planning. Therefore, bankruptcy forecasting is the most important prerequisite for bankruptcy prevention. Due to this issue, the main aim of this article is the prediction of the economic bankrupt-cy of corporations in the Tehran Stock Exchange using group machine learn-ing algorithms. Financial ratios have been used as independent variables and healthy and bankrupt corporations as research dependent variables. The statistical population of the study is the information of financial statements of corporations on the Tehran Stock Exchange from the years 2004 to 2021. In this study, sampling is not used and corporations include two groups healthy and bankrupt. The bankrupt and non-bankrupt groups are selected based on the threshold of the Springate model. The research findings indicate that the accuracy of predicting the bankruptcy of corporations in the group learning model by stacking method is higher than other used models where the AUC and Accuracy Ratio were 0.9276 and 0.8247, respectively.