درخت حوزه‌های تخصصی

Many studies have been conducted in the field of bankruptcy prediction; But in most of them only financial ratios are used. However, in Iran, many non-financial factors affect bankruptcy. The main purpose of this study is to develop a mathematical model in which financial and non-financial indicators such as management and economics factors are used to predict bankruptcy. In this study, 44 variables that had the greatest impact on bankruptcy forecast were selected and with confirmatory factor analysis, a questionnaire was developed and sent to experts in the fields of management, accounting and economics to rank the impact of these variables. The statistical sample of the study includes 200 bankrupt and non-bankrupt companies listed in the period 2009-2018. After collecting the questionnaires using the OLS regression estimation method, the variables that had a factor load of less than 0.5 were eliminated and in the final model 9 main variables. The research model identified 95% of bankrupt companies and 93% of non-bankrupt companies with 95.4% confidence. Then, for verification, two hypotheses were developed and the model of this research was compared with two existing models. The ability to distinguish bankrupt companies from non-bankrupt ones by our proposed model was 6% more accurate than the Pourheidari et al. model, and 9.4% more accurate than Altman’s model.

This study was an attempt to evaluate the progress of capital market efficiency in Iran. Optimal resource allocation and micro and macro investments play a key role in the capital market. The capital market's main task is to circulate capital and allocate resources efficiently and optimally. The main task of this market is to flow capital and allocate resources efficiently and optimally. Is there a regular pattern for determining the stock price? Market efficiency gains significance as it is important to know what factor or factors are effective in determining the price of the stock in the stock market or whether there is a regular pattern for determining the price of a stock. Thus, this study examined the efficiency of the capital market in Iran. In this regard, the researchers used the daily data of the total index of the Tehran Stock Exchange for 2008-2017. Artificial neural network and time series training tests were used to perform the test. The test results showed weak efficiency in the Tehran Stock Exchange and this inefficiency did not change significantly compared to the first period. In other words, in the Tehran Stock Market, one can predict returns using artificial intelligence.

The aim of this study was to investigate the ranking of effective factors on how to implement timely management of goods and equipment and its evaluation criteria in the power distribution company of the whole country using Analytic Hierarchy (AHP). Five dimensions (production technical factors, managerial level management factors, factors related to IT processes and infrastructure, factors related to processes and factors related to training and manpower) were determined and the research hierarchical tree was drawn. Based on the opinions of experts in this tree, five Next, on how to implement timely management of goods and equipment in time and its criteria interact with each other. To determine their weight, the combined approach of fuzzy AHP and fuzzy DEMATEL was used. The influential and expressive dimensions of each other are the cause, and the dimensions of the factors related to IT processes and infrastructures are part of the influential or expressive dimensions of other effects.

The purpose of this study is to present and explain the paradigm model of the role of behavioral characteristics and financial literacy of real investors on the components of their financial management behavior in the Iranian capital market. For this purpose, a 77-item questionnaire derived from qualitative studies using grounded theory method was used. The chi-square value of the model is equal to 3946.370, the degree of freedom of the model is equal to 2174, the result of which is equal to 1.815, and the fit indices of the original model are all in an acceptable and appropriate level. Behavioral characteristics and financial literacy can predict financial management behavior and help real investors analyze stock market trends before making a decision, which leads to investment profitability, financial security and capital satisfaction.

Management of innovation processes is one of the functions of local governments and, therefore, they should be the initiators and moderators of communication between research organizations and enterprises. The program formation of agglomeration innovative development involves the creation of promoting innovation body, which allows to achieve the maximum involvement degree of all the participants in the innovation process. The article is devoted to the research of urban agglomeration innovative development, the need to create a special body or center for innovation, which will form a set and interconnected, and will be integrated into the urban agglomeration and carry out innovation and technological activities as part of research and production infrastructure. The article develops a method for predicting the effectiveness of the advancement of this body through the digital space using trend models. It is expected to receive three forecasts: optimistic, realistic and pessimistic. This will accelerate the establishment of links between the players of the regional innovation market and contribute to a qualitative change in the spatial and functional structure of urban agglomerations. The development of information and communication technologies allows to create effective systems that will stimulate the agglomerations innovative development . Therefore, the communicative activities of regional governments should be carried out through the use of information and communication technologies. Thus, the urgency of developing a methodology for assessing the increase of the innovative component of agglomeration economic development is due to the low percentage of implementation research results, low science-intensive gross value added in the Ukrainian regions, the possibility of using information and communication technologies. Increasing the number of targeted visits will simplify and speed up the process of establishing links between innovation market players at the agglomeration spatial level in both the short and long term

The purpose of this study is to develop proposals and recommendations for the implementation of a process model for the development of leadership qualities of public servants and justification of the conditions for ensuring its effectiveness in terms of digital transformation. The relevance of this study is due to the need to ensure development of the process of professionalization of the senior civil service personnel on the basis of development of leadership qualities that will contribute to the effective operation of the civil service of Ukraine, change management and successful implementation of reforms in Ukraine, taking into account the best world practices. The methodology for assessing the level of managerial competencies of public servants according to the degree of implementation of strategic (key) competencies has been developed. The assessment of managerial competencies according to the degree of their significance for civil servants, the expert group identified the most important management competencies. An approach to understanding has proposed interaction of leadership competencies with managerial competencies, a diagnostic model for assessing the leadership of public servants has been developed. To implement the model, a system of indicators has been developed - single, complex and integrated indicators of civil servants' leadership, using tools: a tree of civil servants' leadership indicators, matrices for the calculated civil servants' leadership indicator, measurement scales for the corresponding level of indicators.

The purpose of this study is to study the indicators for evaluating the effectiveness of the implementation of investment processes on the securities market, taking into account the scientific foundations of information management systems and analysis of indicators of financial efficiency of the investment function of the securities market in Ukraine. The relevance of this study is due to the growing importance of management information systems in all sectors of the Ukrainian economy, in particular, the provision of solutions to the problems of activating investment processes in the securities market of Ukraine by analyzing and reassessing the effectiveness of investment processes at this level, taking into account the scientific basis of management information systems.  A set of indicators that best reflect the implementation of the investment function of the Ukrainian securities market is proposed. A matrix of characteristics of investment processes in the securities market is proposed. It is argued why domestic and foreign investors prefer local securities market indices when making investment decisions. Through the implementation of correlation-regression models, it has been proven that, on average, 87% of changes in investments in securities are due to changes in the number of licensed entities, which on the Chedoch scale indicates a close relationship between the indicators. The results obtained using statistical inference methods indicate a high impact of both external macroeconomic factors that inhibit the development of the securities market and internal, which in turn is reflected in the indicators of assessing the effectiveness of investment processes in the securities market.

With the development of a new stage of the industrial revolution, the importance of digitalization of business development tools is growing. The purpose of this article is to study the applied aspects of digital marketing tools usage for business development in the B2C market. To achieve the purpose and objectives of the study general and special methods are used: comparative analysis of the results of economic and statistical surveys; method of expert assessments by questionnaires using a 5-point Likert scale. The concordance coefficient was used to determine the consistency of the experts' opinions taking into account the related ranks in method of expert assessments. According to the results of the research, it is established that the Ukrainian business of the B2C sector was actively mastering digital marketing tools. The analysis of penetration level of digital technologies in the development of trade business showed the emergence of basic conditions for updating marketing tools to influence the B2C market. There is a rapid coverage rate of multi-purpose use of the Internet among consumers and businesses; gradual growth of digital skills among practitioners; positive dynamics of development of interactive services in the trade sphere. However, the level of use of the retail businesses websites remains low in many spheres of customer service. An important trend of the current development stage of the consumer market is the usage of business Internet platforms designed for mass dissemination of information. Effective marketing channels of interaction with consumers include social media (social networks, blogs or microblogs, websites with multimedia content, knowledge sharing tools), websites, e-shops, and sales via mobile devices. According to the results of expert evaluation, foreground digital technologies, which are able to bring business to a qualitatively new level of interaction with consumers and the provision of trade services have been identified. These are artificial intelligence and cognitive technologies, BigData, Internet of Things (IoT), and cloud computing. The structural and logical scheme of research of digital marketing tools is used for business development which includes two stages is offered. In the first stage, trendwatching, benchmarking and evaluation of internal opportunities for the use of digital marketing tools are performed. In the second stage, three components of digital readiness of business are defined: technological; competence; institutional. The obtained results form the basis of further research to determine the priorities of adaptive digital business behavior for the productive use of existing digital opportunities.

On a global scale, one of the prevalent causes of dementia is Alzheimer’s disease (AD). It will cause a steady deterioration in the individual from the mild stage to the severe stage, and thus impair their capacity to finish any tasks with no aid. The diagnosis is done with the utilization of existing methods which include medical history; neuropsychological testing as well as MRI (Magnetic Resonance Imaging), a lack of sensitivity as well as precision does affect the consistency of efficient procedures. With the deep learning network’s utilization, it is possible to create a framework for detecting specific AD characteristics from the MRI images. While automatic diagnosis is done with the application of diverse machine learning techniques, the existing ones do suffer from certain constraints with regards to accuracy. Thus, this work’s key goal is to increase the classification’s accuracy through the inclusion of a pre-processing approach prior to the deep learning model. The Alzheimer's disease Neuroimaging Initiative (ADNI) database of AD patients was used to develop a deep learning approach for AD identification. In addition, this study will present ideas for Haralick features, feature extraction from Local Binary Pattern (LBP), Artificial Neural Network (ANN), and Visual Geometry Group (VGG)-19 network techniques. The results of the experiments show that the deep learners offered are more effective than other systems already in use.

The complexity of the competitive business environment has highlighted the need to be aware of the organization's strengths and weaknesses and to continuously improve processes. Therefore, managers are looking for a solution for performance measurement of their organization to be able to promote and improve their organization. Evaluating the performance of listed companies in the stock exchange organization is important because in addition to the managers of the organizations, stock traders can also evaluate the companies and make the necessary decisions about holding, selling or buying the shares of these companies in a timely manner. One of the organization's performance measurement solutions is to use financial ratios. Given that a separate study of financial ratios does not provide a correct view of the efficiency of the organization, so the aggregation of the effect of financial ratios seems to be effective. DEA and MCDM are suitable because they enable the achievement of performance index by considering several factors simultaneously, so the performance obtained from this method is reliable. the main purpose of this study is to ranking pharmaceutical companies in the Tehran Stock Exchange between the years 2018 and 2020 using DEA approach and MCDM to provide a single ranking through the Copeland method.

Nowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hybrid network based on amount month demand in every ATM device based on transaction mean of 9 months for 1377 devices to obtain customer behavior patterns, to do so, first designed a basic model based on an auto-regressive with exogenous input network (NARX) then, the optimization of the weight and bias of the designed network is made by the genetic algorithm (GA). As a result, finding the weights of the network represents a nonlinear optimization problem that is solved by the genetic algorithm. Paper results show that the NARX-ANFIS Hybrid network using GA for the learning of rules and to optimize the network weights and weights of the network and the fixed threshold can improve the accuracy of the prediction model. Also, classic models are more efficient and increased benefits and lower financing costs and more rational inventory cash control. As well, the designed model can lead to increase benefits and decrease costs in the bank so that, exact forecast and optimal cash upload in ATMs will lead to increase funds on the bank and rise customers and popularity the brand of the bank.

The increase in non-performing loans considerably reduces profit in the banking systems. To solve these problems, factors influencing the collection of non-performing loans have to be examined in a hopeful attempt to reduce it to an ac-ceptable level. The present study is conducted to analyze and study factors influ-encing the collection of non-performing loans in Shahr Bank. This research is an applied study regarding its goal. It was conducted in two sections, namely the qualitative and quantitative sections. In the qualitative sections, the factors influ-encing the collection of receivables were identified using the theoretical literature and interviews with senior managers of Shahr Bank through the encoding process. In the quantitative section, data was collected by surveying the opinions of 12 experts, including senior managers of Shahr Bank in 2020 using a questionnaire. Thereafter, the factors were selected using the fuzzy Delphi technique and the relationships between them were determined using the fuzzy DEMATEL method. Finally, the factors were weighted and prioritized using the fuzzy ANP method. The research findings showed that non-performing loans can be collected through six types of factors including organizational, regulatory, customer, banking, envi-ronmental, and operational factors. The environmental factor is the most influential factor, while the operational factor is the most influenced and most important factor. In addition, behavioural, contextual, and structural sub-factors have the highest level of importance in the collection of non-performing loans in the order mentioned. These findings can help bank managers make decisions to improve the collection of receivables.