نادر رضایی

Stock market is affected by news and information. If the stock market is not efficient, the reaction of stock price to news and information will place the stock market in overreaction and under-reaction states. Many models have been already presented by using different tools and techniques to forecast the stock market behavior. In this study, the reaction of stock price in the stock market was modeled by the behavioral finance approach. The population of this study included the companies listed on the Tehran Stock Exchange. In order to forecast the stock price, the final price data of the end December, March, June, and September 2006-2015 and the stock prices of 2014 and 2015 were analyzed as the sample. In this study, Bayes' rule was used to estimate the probability of the model change. Through this rule, the probability of an event can be calculated by conditioning the occurrence or lack of occurrence of another event. The results of model estimation showed that there is the probability of being placed in high-fluctuated regimes (overreaction) and low- fluctuated (under-reaction of stock price despite the shocks entered to the stock market. In modelling with the 4-month final prices, it was proved that the real stock price had no difference from the market price.

Organizations expose to financial risk that can lead to bankruptcy and loss of business is increased nowadays. This may leads to discontinuity in operations, increased legal fees, administrative costs and other indirect costs. Accordingly, the purpose of this study was to predict the financial crisis of Tehran Stock Exchange using neural network and genetic algorithm. This research is descriptive and practical and in order to collect data Stock Exchange database software has been used. For data analysis, we used artificial neural network in base form and artificial neural network mix with genetic algorithm. In addition for methods comparison, determination coefficient, Mean squared error and Root-mean square error have been used. The result of study shows that the best artificial neural network is a network with a hidden layer and eight neurons in the layer. This network could predict 97.7 percent of healthy and bankrupt companies correctly for test data. Furthermore the best mixed neural network with genetic algorithm is a network with 400 replications and population size 50, one layer and eight neurons which could correctly predict 100% of healthy and bankrupt companies. Finally, comparison of results of two methods shows that the best method for predicting financial crisis is mixed neural network with genetic algorithm.

Negative relation between unemployment and inflation is known as the Philips model, which states, when inflation is high, unemployment gently decries and when unemployment is low, salaries will increase rapidly. While economic planners of the country tend to impart this situation highly, it would fall down, because frequent increase of inflation with the hope of keeping unemployment low permanently, ultimately will cause rise in expected inflation of organizations and lead to change in their recruitment decisions. In a section of this model, it has also analyzed, relation between income, investment, and consumption. Regular Philips model with derivative deals with first and second level of calculations, however this model has faced many weaknesses and deficiencies in economic cycle of Iran. Regarding its implementation in the economy of Iran, in this research, through substitution of Caputo fractional-order derivatives, we reach out “Philips model of Fractional-order”. Moreover, by numerical calculation of resulted equations and using Maple software, we over reach to Philips curve of Fractional-order in multiple various orders. According to achieved results, this model highly depends on derivation orders and different outcomes will be acquired through various derivation orders. Acquired model in the economy of Iran (situational format in Sugar and Sugar cube Industries) has been studied through phasic technic which proves meaningfulness of relations between variables of the model.

The primary purpose of this research is to investigate the impacts of corporate governance on credit risk in the Iranian banking industry. The sample consists of 20 banks listed on the Tehran Stock Exchange during 2011-2016, using panel data. In this research, credit risk and corporate governance are the dependent and independent variables, respectively. The meta-synthesis method was used for compiling a checklist of corporate governance indicators. Then, the content analysis method was applied for measuring the corporate governance index; i.e., the number of dimensions disclosed on the total number of disclosable dimensions. The results indicate that after adjusting the control variables namely the size, the financial leverage, the ratio of capital adequacy, the GDP and inflation, there is a significant negative relationship between corporate governance quality and the credit risk, which means more effective corporate governance will reduce information asymmetry, increases the clarity and stakeholder confidence, and finally reduces banks’ credit risk. Accordingly, the final recommendation is to reduce credit risk by improving the mechanisms of corporate governance in the Iranian banking industry.

Services are important major element of the economy in today's societies, and banks as one of the most important service organizations direct and support many of the community's economic activities. The purpose of this study was to develop an optimal model for East Azarbaijan banks' performance based on organization risk management using the standardized questionnaire of Kosovo 2017. In order to achieve this purpose, the director or assistant director, head or deputy head, bank managers and experts of banks were selected for statistical sampling and structural equation Modeling approach was used for estimating the model and tests. Organizational risk management factors including "written job descriptions and resources to describe personnel duties, fraud risk assessment with regard to how management and other employees participate" were assessed as factors af-fecting bank performance. Therefore, the structural problems of the banking sys-tem should be resolved so that this system can function and develop in the future, and consequently, in order to resolve the crisis of the banking system, it is neces-sary to reform the banking system.

Accounting processes and their related decisions play an important role in organizing business. If the organization does not have a comprehensive accounting system, managing it will face many problems such as liquidity deficit, tax penalties and so on. Therefore, optimizing accounting processes is essential for managing an organization and improves the company's performance and greater efficiency for business activities. On the other hand, estimates and subjective judgments in preparing financial statements have caused controversy among auditors and owners. In particular, in cases where the standards are not clear, the use of unavoidable judgment and mentality creates ambiguity in financial reporting. This is while the ultimate value of audit activity is in helping the user to determine the quality of the information received. On the other hand, the financial reporting crises of recent years have focused the attention of researchers and professional associations on increasing the reliability of audit reports and reducing negligence. Therefore, considering the importance of auditing and the quality of financial reporting, this study examines and analyzes auditing strategies and determines the optimal strategy to improve the quality of financial reporting. For this purpose, 120 companies active in the Tehran Stock Exchange, which have a legal and formal audit system and use external audit firms for auditing, are considered as a statistical sample based on the counting method, and the senior supervisors, managers and partners of this Companies have been questioned.

The purpose of this study was to present an optimal model Predicting Future Cash Flows optimized neural network with genetic (ANN+GA) and particle swarm algorithms (ANN+PSO). In this study, due to the nonlinear relationship among accounting information, we have tried to predict future cash flows by combining artificial intelligence algorithms. Variables of accruals components and operating cash flows were employed to investigate this prediction; therefore, the data of 137 companies listed in Tehran Stock Exchange during (2009-2017) were analysed. The results of this study showed that both neural network models optimized by genetic and particle swarm algorithms with all variables presented in this study (with 15 predictor variables) are able to provide an optimal model Predicting Future Cash Flows. The results of fitting models also showed that neural network optimized with particle swarm algorithm (ANN+PSO) has lower error coefficient (better efficiency and higher prediction accuracy) than neural network optimized with ge-netic algorithms (ANN+GA).