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Open Access Article
1 - Application of Resource-Based View Theory in Assessing of Efficiency of Companies Accepted in Tehran Stock Exchange by Data Envelopment Analysis
Zahra Moradi Mona Ghilavi Mohammad Hamed Khan Mohammadi Zohreh HajihaResource-based view (RBV) theory analyzes and interprets company's resources in order to find out how organizations gain a competitive advantage. This theory fo-cuses on the implications of the complicated features of a company as the resources for excellent performance MoreResource-based view (RBV) theory analyzes and interprets company's resources in order to find out how organizations gain a competitive advantage. This theory fo-cuses on the implications of the complicated features of a company as the resources for excellent performance and comparative advantage. According to this view, capa-bilities and resources of the company are the main factors in explaining the func-tional results or competitive advantage. Resources can be considered as inputs which enable companies to do their activities. The purpose of this research is to introduce a resource-based theory for calculating the efficiency score of accepted companies in Tehran Securities Exchange by using Data Envelopment Analysis (DEA). In this regard, the financial statements of 190 companies accepted in the exchange for the 2009 – 2018 period have been analysed. Efficiency indicators, which include 4 categories of resources (10 inputs) and 5 outputs, have formed the axis of the mentioned technique. The results of implementing this model for compa-nies with the efficiency score of one, indicates first a minimal input consumption compared to competing companies in the same industry while producing more out-put, and second, using the resource-based view theory (integration of tangible and intangible resources) enables the company to push the boundaries of efficiency. Finally, it can be said that utilizing minimum and maximum resources simultaneous-ly leads to a focus strategy-type competitive advantage. Manuscript profile -
Open Access Article
2 - Optimal Banking Performance Model based on ERM
Ali AfruozianAzar nader rezaei Zohreh Hajiha asghar pakmaramServices 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 Azar MoreServices 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. Manuscript profile -
Open Access Article
3 - The Evaluation of the Capability of the Regression & Neural Network Models in Predicting Future Cash Flows
Bahman Talebi Rasol Abdi Zohreh Hajiha Nader RezaeiCash flow and profit are two important indicators for measuring the performance of a business unit. The future prediction was always a necessity in everyday life, and one of the subjects in which “The Prediction” has a great importance is economical and fina MoreCash flow and profit are two important indicators for measuring the performance of a business unit. The future prediction was always a necessity in everyday life, and one of the subjects in which “The Prediction” has a great importance is economical and financial problems. The purpose of the present study is to predict future cash flows using regression and neural network models. Sub – separated variables of the accruals and operational cash flows were used to investigate this prediction. For this purpose, data of 137 accepted stock exchange companies in Tehran during 2009 to 2017 has been studied. In this study, Eviews9 software for regression model and Matlab13 software for Multi-Layer Artificial Neural Networks (MANN) with Error back propagation algorithm were used to test the hypotheses.The findings of the research show that both regression and neural network models within proposed variables in the present study have the capability of predicting future cash flows. Also, results of neural network models' processes show that a structure with 16 hidden neurons is the best model to predict future cash flows and this proposal neural network model compared with regression model in predicting future cash flows has a better and accurate function. Furthermore, in this study, it was noticed that accruals of assets compared with debt accrual and variables of operating cash flows with accrual components were more predictive for future cash flows. Manuscript profile -
Open Access Article
4 - Modelling Optimal Predicting Future Cash Flows Using New Data Mining Methods (A Combination of Artificial Intelligence Algorithms)
Bahman Talebi Rasoul Abdi Zohreh Hajiha Nader RezaeiThe 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 MoreThe 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). Manuscript profile -
Open Access Article
5 - Forecasting Stock Trend by Data Mining Algorithm
Sadegh Ehteshami Mohsen Hamidian Zohreh Hajiha Serveh ShokrollahiStock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock t MoreStock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It should mention that this research has two hypotheses. It aimed at being practical and it is correlation methodology. The research performed in deductive reasoning. Hypotheses analyzed based on collected data from 180 firms listed in Tehran stock exchange during 2009-2015. Results indicated that algorithms are able to forecast negative stock return. However, random forest algorithm is more powerful than decision tree algorithm. In addition, stock return from last three years and selling growth are the main variables of negative stock return forecasting. Manuscript profile
List of Articles Zohreh Hajiha
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