رحیم قاسمیه

Portfolio optimization as a popular research field has received many attention from researchers in recent decades. Quality of portfolio optimization helps investors generate more sustainable returns. In this research, Convolutional Neural Network (CNN) is used to build a portfolio optimization model based on prediction. This model not only benefits from deep learning technology, but also benefits from modern portfolio theory. In this approach, CNN is first used to predict the future return of each stock. Then, the prediction error of CNN is used as the risk measure of each stock. Integrating the predicted return with the semi-absolute deviation of the prediction error leads to the construction of the portfolio optimization model. This model is compared with an equally weighted portfolio whose stocks are selected with CNN. Also, two prediction based portfolio models with support vector regression (SVR) are used as benchmark portfolios. The empirical data of this research includes the companies in the index of 50 most active companies of Tehran Stock Exchange. The experimental results show that the prediction-based portfolio model with CNN shows a superior performance compared to SVR in the conditions of different returns. Also, the increase in the expected return can improve the performance of this model. This research clearly states the positive role of deep neural networks (DNNs) in creating portfolio optimization models.

various related scenarios. The study aims to identify and assess different financial and investment policies that could enhance investment attraction in Iran's oil industry.

The study employs system dynamics and content analysis to develop the model. The statistical population for the content analysis includes upstream oil industry documents and related articles on investment. In the quantitative section, employees, managers, and experts in the oil industry were selected as the sample population. Data analysis was conducted using Vensim software, and nine different scenarios were developed to evaluate financial and investment policies.

The results revealed that intermediary financing, with an increase of 128.854 units, was identified as the most attractive policy. Debt-based financing, with an increase of 108.333 units, and financing based on underground reserves, with an increase of 69.580 units, followed. These findings highlight the importance of diversifying financial resources and utilizing innovative financial structures in Iran's oil industry.

The study concludes that a combination of different financial resources and the application of diversified financial policies can significantly contribute to attracting more investments and reducing financial risks in Iran's oil industry. These findings align with previous studies on financial and investment policies and provide valuable insights for policymakers in Iran's oil sector.

The main reason for people investing in the stock market is to make a profit, which requires accurate information about the stock market, price changes and predicting its future trend. Therefore, investors need powerful and reliable tools for forecasting Stock prices in the future. The main purpose of this study is to present a method based on wavelet denoising and dynamic time warping to identify the stock price pattern in the Tehran Stock Exchange. In this regard, first, using the wavelet denoising preprocessing step, noise is removed from the stock price time series, and then the extracted data is used as input to the dynamic time warping prediction model. MATLAB software version 9.11 was used to analyze the research data. The statistical population of the present study includes 3 shares among the shares of steel industry companies of Tehran Stock Exchange. The research was conducted in the period 1395 to 1398. The results show that the predictions obtained from the dynamic time warping method equipped with the wavelet denoising preprocessing step in comparison with the predictions obtained from the dynamic time warping method without the wavelet denoising preprocessing step in all three shares studied, have been associated with much less accuracy and error.

IntroductionThe research’s aim is to evaluate meta-innovative methods to predict stock price behavior and introduce the most efficient method in the Iranian stock market. Due to the uncertainty in the field of investment and the multitude of variables, investors turn to the forecasting method, by which their estimates are close to reality and their errors are reduced. Forecasting the stock market has many complexities as it is affected by a number of economic and non-economic factors such as market news, political events, social chaos and so on (B. Labiad, 2016). Due to the volatility of the stock market, predicting stock price changes in daily trading is a challenging task and it is one of the most important issues in the financial world that has attracted the attention of most financial analysts and researchers. Financial markets are an attractive field for investment in order to achieve high profits but obtaining high profits in this market is not easy, because some data are dynamic, non-linear, and variable, and unstable in nature, and therefore there are many risks in this way (Lahmiri & Boukadoum, 2015). Recently, computational intelligence (CI) techniques such as artificial neural network (ANN), nature-inspired and fuzzy algorithms have been developed for stock price prediction due to their ability to handle noisy data in the financial market(Lahmiri & Boukadoum, 2015). In many cases, ANNs suffer from limitations such as local minima, slow convergence, long training time, and fitting difficulty due to the large number of parameters to adjust(Chandar, 2018). These problems have been neglected in much previous research. Therefore, the main goal of this research is to develop a forecasting model consisting of artificial neural networks and nature-inspired algorithms to investigate the above issues and improve forecasting accuracy. In this regard, this research seeks to answer this question: Can the hybrid models of neural networks and meta-heuristic algorithms provide accurate stock predictions?Methodology In this study, the stock prices of 5 listed companies in the base metals index of Tehran Stock Exchange in the period 1396 to 1398 were predicted. For this purpose, the optimal variables are selected from among 9 primary and widely used variables using feature selection methods, super-innovative algorithms of Harris and Wall's algorithms and then using Back propagation neural networks, radial base neural network and neural network with time delay Stock prices were predicted. Focusing on basic metal companies in Tehran Stock Exchange as a statistical population, 21 companies with the highest correlation coefficient were finally selected as a sample. To generalize the sample as much as possible to the society from among the companies, 5 companies that included small, medium, and large companies were selected as samples. The two symbols Zangan and Faravar were among the smallest companies and the two companies Femli and Foulad were among the largest companies, which were included in the sample as representatives of small and large companies, respectively, and the symbol of Fasmin was included in the sample as a representative of medium-sized companies. The required data: opening price, closing price, highest price, lowest price, closing price, volume of transactions, value of transactions, number of transactions and range of changes were collected from Tehran Stock Exchange Technology Management Company (www.tsetmc.com) and Codal website. MATLAB software was used to perform calculations and implement the proposed method.Results and Discussion The results show that in the symbol of Zangan, the combined models HHOBP, WOARBF and HHOTD have the highest R2 coefficient, respectively. In terms of MSE and MAE criteria, the HHOTD model has the lowest error rate and the lowest absolute average of the difference between the actual and predicted data, which shows that in this model, the predicted values are closer to the actual values. According to the Hitrate criterion, the HHOTD model has the highest correct estimation (89%) compared to the rest of the models. With these results, it can be concluded that Harris Hawk algorithm has the highest accuracy and the neural network with time delay has high efficiency.Conclusion Since linear models do not have the ability to understand and extract non-linear patterns such as stock price time series, the goal in this research was to combine artificial neural networks and meta-heuristic algorithms to accurately predict stock price trends. In this research, first, the optimal variables were selected using feature selection methods, meta-heuristic algorithms of Harris hawk and Whale. Then, by using neural networks after propagation of error, radial basis neural network and neural network with time delay, the stock price was predicted. Considering the findings, it is suggested that the Harris's hawk evolutionary method is superior to the whale's evolutionary algorithm. Proper navigation of the problem space and finding features is the reason for the superiority of Harris's hawk results compared to whale's algorithm. It was also shown in this research that networks with time delay have higher efficiency. Findings indicate that the hybrid method of Harris hawk and neural network with time delay is the most effective.  It is suggested to use a hybrid model of post-error propagation neural network, radial base neural network and time delay neural network with evolutionary algorithms such as genetics in future research. Also, it is suggested to use other predictive algorithms such as support vector regression in combination with neural network with time delay.

The purpose of this research is to identify the effect of price acceleration in Tehran Stock Exchange. For this purpose, the data of 60 companies available in the stock exchange during a 10-year period (2012-2021) has been used. The regression model of the research has been tested using the time series data method. The hypotheses of this research have been examined in two boom and recession periods. In investigating the problem of risk, Fama and French's five-factor model was used to explain the effect of price acceleration, which shows that this model cannot explain the effect of price acceleration, and this result is still valid in the boom and recession periods, although it has a higher explanatory power in the boom period. Will have. In examining the second hypothesis, the five-factor model was expanded to measure the ability of the profit acceleration factor to explain the price acceleration effect by adding this factor to the model. The results of the hypothesis test show that the 6-factor model does not have sufficient ability to explain the effect of price acceleration, and this result has been confirmed in the investigation of an event, such as a downward trend of profit acceleration has been observed, in which the reaction is less than the limit to profit news. It is not visible. These results are still valid in two periods of boom and recession, although only in the j6k12 strategy in two periods of boom and recession, the second hypothesis was confirmed and it was observed that in this strategy, profit acceleration is a suitable behavioral factor to explain the effect of price acceleration, because by adding this factor According to the model, the model has an explanatory power of up to 59%.

Liquidity in the stock market expresses how close the stock is to cash. Since the Tehran Stock Exchange is included among the world's non-cash stock exchanges and the issue of stock liquidity is one of the main concerns of investors, therefore, in this research, an attempt is made to predict the liquidity of the stock exchange using deep learning models. The statistical population includes companies active in the Tehran Stock Exchange in the years 1394-1400, and 23 companies were studied as a sample. The transaction volume and value, stock turnover ratio, Amihood, the difference between the bid and ask prices (spread) and the relative spread were measured as liquidity measure and a fully connected neural network based on multilayer perceptron (MLP), mixed deep learning (MDL) model and classical linear regression (LR) model was tested. To measure the predictive power of the models, the mean squared error (MSE) and mean absolute error (MAE) measures were calculated, and t-test was used to compare the accuracy of forecasting methods. According to the results, the prediction error rate of MDL model is lower than the other two models, and the statistical tests also confirm the significance difference in the prediction accuracy of the models at the 95% confidence level, which shows the proper performance of the proposed hybrid model compared to two other models.

Since volatility measurement plays an important role in risk assessment and uncertainty in financial markets, this study provides an appropriate method for predicting stock pricefluctuations using the GARCH and Bootstrap Garch method. And then compare the confidence intervals by the two methods. The research data were collected by reviewing the statistics of the companies listed in the list of the top 50 companies in the securities market. The results show that the confidence interval of the Bootstrap Garch method is shorter than the Garch method,so the Bootstrap Garch method provides a more accurate prediction than the GARCH method. In addition, it is usually expected to increase with the increase in horizons of prediction of variance, but this does not occur for the Garch (1.1) method; therefore, it seems that the prediction of the variance of the Bootstrap GARCH model has more compatibility with theoretical evidence.

Innovations based on information technology and its applications have become the main drivers of organizational performance improvement, economic growth and social change. Despite significant growth in this area, the importance of complementary factors such as corporate entrepreneurship in increasing the impact of technological innovation on organizational performance has not yet received much attention. This study develops and tests a framework in which the nature of the relationship between ICT use and organizational performance in small and medium-sized enterprises has been examined. This research is a survey in terms of the type of descriptive research and an applied research in terms of purpose. Considering Ahvaz Industrial Town No. 1 as a statistical population, a total of 210 were considered as samples and their information was collected through a questionnaire. Smart PLS software was used to analyse the proposed relationships with a focus on the importance of the mediating impact of organizational entrepreneurship and innovation. Multi-group analysis investigated the effect of the level of information technology use on the confirmation model and confirmed 9 hypotheses out of a total of 16 hypotheses. Research variables have predicted and reflected a total of 52% of performance changes. The results, while confirming the model, confirm the need for a proper information and communication system in companies.

 The increasing trend of food waste is one of the serious challenges in mostcountries, especially developing countries, to meet the sufficient food needs of society. In thisstudy, the thermodynamic performance of two traditional bread production chains isexamined. These two chains contain the fresh bread production and the double-baked breadproduction chain (without waste).

The calculations are based on the processing of 1000 kg of doughfor all eight bread chains. Lavash, Barbari, Tafton and Sangak breads produced had a mass ofabout 0.145, 0.410, 0.220 and 0.500 kg, respectively. In order to make an exergic comparisonbetween the bread production chains, the moisture content in both technologies for all Lavash,Barbari, Tafton and Sangak breads were 9.5, 34.1, 19.9 and 17.8 percent, respectively. Dataanalysis was done using two software “ees” and “ e! Sankey “.

The chemical exergy flows were found to be much larger than the physical exergyconsumed in all the bread chains studied. In the traditional bread production chain, lavashbread, baked twice, with the amount of 78.52%, Sangak with the amount of 76.06%, Barbariwith the amount of 70.64% and fresh Sangak bread with the amount of 70.29% had the bestexergy performance. The lowest total lost and irreversible exery were found in 1000 kg ofprocessed dough in the following breads: double-baked Lavash with the amount of (2910megajoules), Sangak bread with the amount of (3288 megajoules), fresh Sangak bread withthe amount of (3862 megajoules). And Taftoon bread double-baked (3921 megajoules).Lowest specific exergy loss and best thermodynamic performance was observed in doublebakedLavash bread (3.80), Sangak bread (3.91), Tafton bread (5.23) and Barbary bread(5.24), respectively. Combining exergy analysis and its application in the field of supply chainmanagement and management sciences are among the innovations of the present study.

Most of the energy loss (non-material) occurs during the cooking, cooling, andfreezing stages. Therefore, any progress in the production of traditional bread should befocused on the design of effective thermodynamic processes of these three stages and the useof appropriate technologies.

The road to excellence for an organization is a continuous process that begins with planning and goal setting and continues with ongoing evaluations, necessary reforms, removal of barriers and reinforcing strengths. In this regard, the full understanding of the current status of the organization and its problems is a crucial step. Therefore, managers implement their strategic thinking within the framework of strategic planning, which is the process of prioritizing the missions of the organization. Due to the important and influential role of District 5 of Gas Transmission Operations in Iran, it needs strategic planning to achieve its mission. Meanwhile, a new strategic plan has been developed. In the current study, the SWOT technique was used to identify the current status and goal setting, while the SWARA method was used for weighting the criteria. The results showed the score of 2.47 for internal factors and 2.53 for external factors. The ultimate goal was to reach a score of 3 for both internal and external factors. To this end, physical asset management and motivating personnel are the most important issues addressed in the strategic plan. In this way, transparency, flexibility, the support and acceptance of new ideas, reliance to domestic companies, following environmental regulations, and respecting the efforts of employees are among the values in reaching the vision. Therefore, by consulting the experts and in line with the world?s established standards, the District 5 has taken steps to implement the developed strategies and good results have been achieved so far.

Today, one of the pervasive issues is damage of ozone layer and environmental pollution due to production of hazardous wastes resulting from human activities in different industries. Hospitals, due to production of infectious and hazardous wastes have been identified as the second polluting industry. This study aims to develop standards for green management of hospitals, by using concepts of green supply chain management, to promote green activities in Bushehr hospitals. After reviewing the literature and researches related to green hospital, related factors were refined and the data collection was conducted through questionnaire. Finally, to analyze the data and responding to research questions Amos software was used. The study population was all personnel of Bushehr hospitals, who involved in the care process. After analyzing the data, the weights of factors affecting the standards of green hospital were determined by SWARA method. The weights of three main selective variables, including green materials, environment and green technology were calculated. Green management and social responsibility with (0.35186) and green materials with (0.31928), has the highest and the least effect on green hospitals respectively. Among three main variables, green management and social responsibility were identified as the most effective index of green hospital standards.

Multi-sided assembly line is a typical production line in factories, where tasks are performed parallel in different sides of the assembly line. This type of line is normally found in producing large products such as cars. It is very important for the production line to be balanced in order to improve the production productivity. This paper presents a new approach based on simulated annealing algorithm to vertical balancing of multi-sided assembly lines. Zoning constraints, cycle time, working time and precedence relationships are considered as hard constraints while positional constraints are considered as soft constraints. To show the applicability of the proposed approach, it is applied on a real sample assembly line. In order to find the most suitable values for the parameters of the algorithm, different scenarios have been run on the sample assembly line. Findings indicate that the proposed approach is highly capable to achieve the predetermined goals of the line balancing problem.

Machinery selection is an important issue in manufacturing and industrial systems, because an incorrect selection might end up to many problems which have negative impact on productivity, precision, flexibility, and the quality of products. Because of different influencing factors, it is required to consider both managers and experts for acceptable tolerance in measuring indices. In this research, an integrated approach using correlation coefficient weighting method, standard deviation, and axiomatic design enhanced with fuzzy numbers is proposed for the machinery and equipment selection problem. The proposed approach is applied to a real problem in Sairan Company. Results show that the application of the approach is satisfactory for both managers and experts and is implementable in machinery selection decisions.

Among the current production management issues, the ability of handling the unexpected changes, survive the unexpected threats in business market and suitable use of environmental fluctuation as an opportunity are the most challenges of the production organizations. This ability is agility. The first step to achieve the agility is to measure the current agility level of organization. As each organization has its own structure, culture, strategies and goals, the agility differs from one organization to others. Furthermore, the required agility level differs from one organization to another regarding the competition environment in which they compete. In this paper, a model presented in order to measure the agility level in Shiraz municipality waste management organization. Based on this goal, first through the comprehensive literature review, different agility model studied and relevant dimensions and components of agility measurement model extracted using meta- synthesis method. The main criteria and the principal components of these models include Agility drivers, agility enablers, key capabilities, agility strategies, required agility level, agility goals and results. Then all levels of model (three levels) have been Finalized using Focus Group method and the agility measurement tools designed according to third level of model (Guide tips). Finally, the agility level of Shiraz municipal waste management organization measured and results show a moderate level of agility in this organization.

With the urban population growth, and accordingly the increase in road traffic, one of the most important factors is to find a suitable solution to decrease the intra-city response time; thus, it is highly important to send a team to the affected areas in order to evaluate the loss and estimate the demand. Since time is critical in responding to crisis, routing of assessment teams was considered as a problem. The literature review shows that a very few studies have addressed the mathematical modeling in crisis management. However, this research aimed to find shortest possible route for Damage Assessment Team in order to assess damages in the affected area. So that the assessment team visited all the main points of the region only once and finally return to starting point. In this study, Bahmani region in Bushehr city was studied and selected by using operations research models for routing earthquake damage assessment teams and the main points of this region were identified as the nodes of the model (19 nodes in total). By using Lingo software, the mathematical model was coded with18-nodes and 28-existing paths between these nodes and the shortest path to all these nodes were identified. According to the results, this research may develop an effective guide for crisis management of the province; as a result fast relief provides and fatalities will be reduced by identifying the shortest route.

Supply chain management as a vital challenge to the Cement industry and developing infrastructure as a whole has been posed by scholars. This research is an attempt to investigate LARG supply chain management as a comprehensive approach to consider simultaneously four basic relevant strategies namely Lean, Agile, Resilience and Green. Using a survey methodology and gathering data from twenty one experts in cement industry, every strategy has been weighted by SWARA and VIKOR techniques. Then results were compared utilizing COPRAS- techniques. Finally, the average score was led to conclusion that in the cement industry the most important strategy are resilience, green, agile and lean respectively. At the end a set of useful guidelines were developed and recommended to the managers.

the increasingly rate of change and as a result shortening product life cycle, has emphasis on innovation among industrial companies. The purpose of this research is to briefly discuss the role that innovation plays in global competition and to investigate the relationship among influential variables on innovation in petrochemical companies. Drawing from literature review we developed a research model. It consists of 26 factors and then with utilizing DEMATEL Fuzzy technique, they were reduced to seven variables, namely, culture of change, management performance, focusing on clients’ demand, financial conditions, product flexibility, using new technology and government support. Then the effects of above factors on organizational innovation have been assessed. Results emphasizes on vital role of culture of change among employees that must be at center of interest on managers. Also results indicate that clients’ interest is an independent variable that neither effects nor effected by other variables. Among other findings, results indicate that culture of change, management performance and using modern technologies are the most vital factors affecting innovation in petrochemical companies. Investment variable has been identified as an independent variable that has great effect on a number of effective variables in petrochemical companies.

Much of the literature regarding capital structure indicates that not only conflict between manager and owner but also external group impact on funding decisions. It emphasizes that a firm and its competitors are under influence of the method of funding of a firm. Existing literature provides a number of basic approaches for linking market limitations and funding decisions. A large part of empirical literature such as Bolton & Scharfstein (1990), Chevalier & Scharfstein (1996) and Dasgupta & Titman (1998) argue that the impact of financial leverage on performance is related to the degree of competition in capital market. Subsequently, this research is organized to dealing with the issue of whether or not the competition in product market can impact the relationship between financial leverage and performance of the companies listed on Tehran Stock Exchange. In other words, the current research intends to answer if the extent of competition in product market can make any difference on the extent of correlation between financial leverage and performance. Questions and hypotheses: The main question is “Does the degree of competition in product market influence the intensity of financial-leverage impact on performance? Thus the following hypotheses have been developed:Financial leverage has a U-form impact on performance The completion level has a positive impact on performance With respect to the change in competition extent, the impact of financial leverage on performance is made better or worse.

Using Herfindahl-Hirschman’s index, the level of competition has been calculated. Then, considering the level of competition the impact of capital structure on performance, utilizing mix data regression has been examined. Using data of 133 companies during a 5 years period (1385-1389) this research has benefited from time series and also deals with cross sectional data. In order to incorporate time series and cross sectional data, a combination of data should be used. Conclusion and

The results indicate that the financial leverage has a u-shape impact on performance. The level of competition has a meaningful impact on performance. Due to the degree of financial leverage, this impact has a direct correlation with the competition. Also this research shed light on this point that “the impact of financial leverage on performance” is also under the influence of level of competition. This research verifies a direct correlation between level of financial leverage and the level of competition. Also the leverage firms in competitive market show a better performance than a focused (centralized) market.

The aim of this research is to investigate the impacts of Intellectual Capital on Banks’ revenue efficiency during 1385-1389. First of all using DEA technique banks’ revenue efficiency has been computed then the effects of Intellectual Capital and its components on efficiency has examined.The findings indicate that human capital has negative relationship, capital employed and structural capital and intellectual capital have positive relationship with banks’ revenue.

In Islamic-Iranian citizen’s area، the fundamental objective in Perspective Document for Iran has been defined as «to meet religious democratic society with the use of IT in order to facilitate and achieve social justice، promotion of life quality، and citizens'' moral and scientific virtues development. Using fuzzy analytical hierarchy process (FAHP) approach، this research، focusing on five dimensions، namely technological، legal، managerial، cultural and educational infrastructures، intended to classify influential criteria in enabling e-citizens. The research method was descriptive survey and structured questionnaire was used for data collection. Results indicated that technological infrastructures were of greater importance than the others. Also، among the sub-criteria، factors such as internet networks and bandwidth extension، charter and regulations compilation in IT area، the existence of expert and experienced IT managers، privacy security awareness increase، providing infrastructures for schools (software and hardware)، number of IT experts، and interaction training in internet were recognized، respectively، as enabling factors in e-citizens training.

Given the importance of social interaction, knowledge management and performance, this study is to survey the effects of internal and external social interaction on the performance of university faculty members, with an emphasis on the mediating role of knowledge management. All 130 faculty members of Persian Gulf University of Bushehr took part in this research. 92 questionnaires were distributed, 64 of which were returned and analyzed by structural equation modeling to help presented hypotheses being tested. The results showed that internal trust and internal and external communication have positive and significant effect on knowledge management at the university. Moreover, knowledge management has a positive and significant effect on the performance of faculty members.

Nowadays entrepreneurship orientation and entrepreneurial activities have become the focus of attention by many researchers because of fast-moving markets and competitive conditions. Corporate entrepreneurship is an imperative for the improvement of organizational performance and organizational growth. The aim of this research is to investigate the effects of corporate entrepreneurship activities on performance of public and private organizations.A survey questionnaire was designed to collect data from 70 respondents (top and middle managers) of different firms. The reliability tests were conducted. The internal consistency of the total score was 0.96. Various statistical tests including correlation coefficient, regression analysis, one way ANOVA, t-test and the like have been conducted to test the six hypotheses. The result indicates that, apart from “risk taking” and “customer satisfaction”, there is a positive correlation among research variables and as to the entrepreneurship and performance there is a meaningful difference between public and private firms. Also only 7.33 percent of organizational performance could be defined by the corporate entrepreneurship.

Growing external competition, decreasing internal expenses and improving productivity, have forced many companies which are involved in supply chain to initiate radical changes and focusing on BPR. Nowadays BPR is one of the favorite ways to optimum management of supply chain management< dealing with challenges and problems. This article not only investigates the concept of BPR but also reviews the most important methods and recent models. Obviously it helps to understanding BPR concept and provides guidance to implementation of BPR in organizations.

No doubt the optimal and efficient use of resources and reach to organizational goals depends on manpower's performance. Iran police force compare to other organizations faces different conditions that require an ability to gather and assess information for appropriate actions. This paper aims to investigate the impact of financial and non-financial incentives on the police force performance. For this purpose, based on in depth literature review, a conceptual model has been developed. Then through a field research and using questionnaire data were gathered. Finally the conceptual model and research hypotheses were tested by using Smart PLS software. The results show that financial and non-financial incentives and job satisfaction have significant effects on police performance. Also financial incentives are affecting on job satisfaction. But non-financial incentives have no impact on job satisfaction.