مطالب

Spatial justice as a new term focuses more on cities, so it is important to address the national scale. Spatial injustice refers to the imbalanced distribution of valuable political, social and economic power, wealth, infrastructures and opportunity resources. From a political geography point of view, spatial injustice can be a threat to the national integrity, national & domestic security and creation of critical areas in the state. The main goal of this paper is Identification and categorization of factors affecting the spatial injustice in Iran. This paper tries to find an answer to the research main question: What are the most important factors affecting spatial injustice in Iran? This study is practical regarding its purpose and in terms of data collection is survey descriptive. Moreover, its data collection is from the questionnaire. First, the dimensions and spatial injustice indicators are distinguished based on the review of the literature, content and comparative analysis of related researches, and interviews with experts. Then using the methodology of modern analytical-interpretive structural modeling (ISM), the relationship between the indicators is determined and analyzed. Finally, the type of variables according to their influence and reception on other variables was identified using MICMAC analysis. Results show that the most important foundations of spatial injustice in Iran based on Interpretive-Structural Modeling Are Inequality in the distribution of power, wealth and opportunities and Theoretical weakness about the spatial justice domain of knowledge

Although theoretical and empirical literature regarding the stylized facts shows evidence of their correlations to herding behavior in financial markets, the causes of such phenomena are still unknown. Using an agent-based model strengthened by the competition co-evolution algorithm (STGP) technique, this study provides laboratory evidence on capital market dynamics and analyses the behavioral foundations of stylized facts such as fat tails, leverage effects, and volatility clustering. The simulated stock markets consist of two groups; the “Best agents”, which are a small portion of artificial agents, and the “Residual agents”, which are the main group of artificial agents. The best performance in terms of breeding fitness returns is the main feature of the “Best agents”. More, the size of the “Best Agents” group is specified as 2.5%, 5%, 10% &20% of the total population size. An agent-based model consists of two portions, a two thousand population of trader agents that each has its decision-making strategy, and a virtual market that creates the trading strategies. Then the model evolved step by step using a feed with real quotes of the financial instruments by Adaptive Modeler. A training period is considered 2500 bars (started in November 2003), and the test period started in December 2013. The observation shows that the herding behavior in the price series created by the “Residual agents” is less than the “Best agents” series. Therefore, the greater diversity of trade strategies as the genetic differences of artificial agents leads to less herding. The observations exhibit that the volatility clustering, leverage effects, and nonlinear dependence are more likely to experience in the price series generated by “Best gents”. Furthermore, observations indicate that if the population is well diversified in terms of trading strategies, the efficiency of the market increases.

The ability to predict the stock market and analyze market trends is invaluable to researchers and anyone interested in investing. However, this task is a challenging problem due to a large number of parameters and unpredictable noise that may affect the stock price. To overcome this issue, researchers have employed numerous approaches such as Moving Average (MA), Support Vector Machine (SVM), and Neural Networks. With technological advances, deep learning methods have become popular in processing time-series data. In this paper, we compare two recently introduced deep learning models, namely a Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting daily movements of the Standard & Poor (S&P 500) index using the daily closing price of this index from 14/5/1991 to 14/5/2021. Results show that both models are effective and accurate in stock market prediction. In this case study, the mean squared error (MSE) and mean absolute error (MAE) for the GRU model are slightly lower than the LSTM model; hence, GRU outperformed the LSTM model despite its simpler structure. The results of this study are applicable in various instances where it is challenging to identify patterns among large volumes of unstructured data, such as medical data analysis, text mining, and financial time series modeling.

Presenting an audit report is the basis for developing competitive strategies that, in the form of operational transparency, are able to guide stakeholders in financial decisions and inform them of operational information and facts. Because auditors have a crucial role to play in reporting these reports, they need to be more balanced, both professionally and on the basis of psychological functions, in finding performance gaps with the functional realities of companies. Therefore, knowing the individual characteristics in this area can help to develop the functions of the audit report. This is because auditors, like any person in charge of a profession, may face disruptions and negative performance characteristics that make it difficult for them to disclose corporate performance facts. Hence this study was to analyze the causes of alienation of conduct in a professional career path for a more comprehensive understanding of the auditor's job is frustration. This research is a mix of methods because through the analysis of grounded theory and with the participation of 17 auditing experts, it seeks to identify the components and statements related to the causes of behavioral alienation in the professional career path of trusted auditors of the Stock Exchange Organization. Then, through the link analysis method and with the participation of trusted auditors of the stock exchange organization, it tries to examine the stimuli and consequences of behavioral alienation in the professional career path of auditors in the form of a systematic representation model. The results obtained from the qualitative part of the existence of three cultural; Social and structural dimensions in the form of 7 categories. The results in the quantitative part also showed that the lack of symbols of professional behavior in the auditors of the stock exchange organization under the component of cultural dimension is the most important functional stimulus in auditors' professional frustrations as causes of professional behavior alienation that can result from this cultural disorder. Cause social conflicts in the professional work of certified auditors of the stock exchange organization.

The purpose of this paper is to focus on examining the impact of conservatism on IPOs underpricing and then examine the role of audit quality as a moderating variable in the relationship between conservatism and IPOs. Based on financial-behavioral theories, analyses are conducted of data from a sample of Tehran Stock Exchange (TSE) listed companies for the fiscal years 2008–2017 (i.e. 230 firm-year observations). Correlation and regression analyses are performed to evaluate possible associations between conservatism and initial public offerings (IPOs) underpricing with regard to audit quality. There is a negative significant relationship between conservatism and IPOs underpricing, i.e., it reduces IPOs underpricing. Also, the research results indicated that the auditing quality cannot moderate the relationship between conservatism and IPOs underpricing in Iran’s stock market. This conclusion may be explained under the winner’s curse theory. Accordingly, informed investors do not request to purchase unattractive stocks, and uninformed investors demand and obtain all the unattractive stocks since there is no competition between informed and uninformed investors. A limitation of this paper is the number of firms, which for future studies needs to be considered. This research is expected to contribute comprehensively to expanding the theoretical foundations and increase audience knowledge of the underpricing of initial public offerings (IPOs). It is also expected that the results of the study will: 1. determine the underpricing of IPOs in Iran during the research period; 2. document the role of conservatism in reducing underpricing of IPOs; 3. potentially prevent inappropriate pricing of new stocks. Furthermore, the findings of this study suggest that the application of financial-behavioral theories calls for more inquiry.