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Innovation is one of the basic tools of growth strategies to enter new markets, increase market share and create a competitive advantage. The purpose of this study is to evaluate the efficiency of innovative techniques in improving new and traditional criteria of ICT field companies’ performance during the years 2013 to 2017. Based on this goal, the research was conducted qualitatively and quantitatively. Based on Cochran sampling, 8 companies were selected as a statistical sample. In the first phase, to identify and determine the centers of decision-making units, inputs and outputs of the departments through Delphi method, distribution of questionnaires and aggregation of opinions of individuals and in in the second phase, the classification of model inputs and outputs and weighting of parameters by Delphi method and network analysis process method, and in the third phase of the research, the efficiency of innovative techniques in improving new and traditional performance criteria of ICT fieldcompanies is investigated. According to the results of the present study and in general among 8 sample companies, the first rank of efficiency is awarded to Iran Arqam Company and the 8th rank is awarded to Iran Telecommunication.

In an expansive test of nations over distinctive geographic districts and over a long period of time, we discover restricted nation and variable-specific impacts of central bank straightforwardness on figure exactness and their scattering among an expansive set of proficient figures of monetary and macroeconomic factors. More communication indeed increments estimate mistakes and scattering. So the aim at this study is a systematic review of central bank transparency and capital market reaction. This study was conducted using a systematic review. At first all papers (n=165) related to keywords (capital market reaction and central bank transparency) searched for Persian and English databases; Google, Google scholar, PubMed covering the period from 2012 through 2022 was performed. Then all qualitative and quantitative papers related to central bank transparency selected and analyzed (n=28). As a result to inclusion and exclusion criteria, papers related to central bank transparency were found and analyzed (n=28). Predefined inclusion and exclusion criteria were: papers related to central bank transparency, papers were Persian and English, types of papers were original and all the papers were free full text. In the initial search, 165 papers were found that after reviewing the titles and abstract articles and removing repetitive and non-related, 54 possible related articles were investigated. Of these, 31 papers were omitted from the abstract because of lack of access to the original article and lack of sufficient information. Finally, 28 papers were included in the study. There are very few studies that have examined the effect of central bank transparency on capital market turmoil. The effect of central bank transpar-ency on the domestic capital market has not been studied. There is no agreement in the literature on theoretical discussions on whether increasing central bank transparency affects capital market turmoil. In foreign research, we can also refer to the study of Dinser and Eichngreen (2007) who have studied this effect empirically using the main transparency index. The pre-sent systematic review shows that almost whole of papers on central bank transparency and capital market reaction highlighted similar findings con-cerning monetary policy decisions.

An investment portfolio is a collection of financial assets consisting of investment tools such as stocks, bonds, and bank deposits, among others, which are held by a person or a group of persons. In this research, we use the Markowitz model to optimize the stock portfolio and identify the minimum spanning tree (MST) structure in the portfolio consisting of 50 stocks traded in the TSE. The observable which is used to detect the minimum spanning tree (MST) of the stocks of a given portfolio is the synchronous correlation coefficient of the daily difference of logarithm of closure price of stocks. The correlation coefficient is calculated between all the possible pairs of stocks present in the portfolio in a given time course. The goal of the present study is to obtain the taxonomy of a portfolio of stocks traded in the TSE by using the information of time series of stock prices only. In this research, report results obtained by investigating the portfolio of the stocks used to compute 50 stocks of the Iran Stock Exchange in the time period from January 2012 to October 2022.

In this article, the pricing of option contracts is discussed using the Mikhailov and Nogel model and the artificial neural network method. The purpose of this research is to investigate and compare the performance of various types of activator functions available in artificial neural networks for the pricing of option contracts. The Mikhailov and Nogel model is the same model that is dependent on time. In the design of the artificial neural network required for this research, the parameters of the Mikhailov and Nogel model have been used as network inputs, as well as 700 data from the daily price of stock options available in the Tehran Stock Exchange market (in 2021) as the net-work output. The first 600 data are considered for learning and the remaining data for comparison and conclusion. At first, the pricing is done with 4 commonly used activator functions, and then the results of each are com-pared with the real prices of the Tehran Stock Exchange to determine which item provides a more accurate forecast. The results obtained from this re-search show that among the activator functions available in this research, the ReLU activator function performs better than other activator functions.

So far, distress prediction models have been based on balanced, such sampling is not consistent with the reality of the statistical community of companies. If the data are balanced, the bias in sample selection may lead to an underestimation of typeI error and an overestimation of the typeII error of models. Although imbalanced data-based models are compatible with reality, they have a higher typeI error compared to balanced data-based models. The cost of typeI error is more important to Beneficiaries than the cost of typeII error. In this study, for reducing typeI error of imbalanced data-based models, random and clustering-based undersampling were used. Tested data included 760 companies since 2007-2007 with 4 different degrees and the results of the H1 to H3 test represented them. In all cases of the typeI error, typeII error of balanced data-based models were lower and more, respectively, compared to imbalanced data-based models; also, in most cases, the geometric mean of balanced data-based models was higher compared to imbalanced data-based models, respectively. The results of testing H4 to H6 show that in most cases, typeI error, typeII error and the geometric mean criterion of models based on modified imbalanced data were less, more, and more, respectiively compared to the models based on imbalanced data, in other words, applying Undersampling methods on imbalanced training data led to a decrease in typeI error and an increase in typeII error and geometric mean criteria. As a result using models based on modified imbalanced data is suggested to Beneficiaries

Cluster fluctuations and fractal structures are important features of space-time correlation in complex financial systems. However, the microscopic mechanism of creation and expansion of these two features in financial markets remains challenging. In the current research, by using factor-based model design and considering a new interactive mechanism called multi-level clustering, the formation process of cluster fluctuations was investigated with regard to the fractal structure of financial markets. For this purpose, the daily information of the final price of 150 shares that were accepted in the Tehran Stock Exchange, after the final screening, was entered in 5 sections with 30 shares in each section, in the desired model, and they were aggregated in three stock levels., sector and market were measured. Due to the fact that some investors have a longer investment horizon in the stock market and due to the limitation of the investigated time period, the maximum investment horizon of 1000 days has been determined in the model.In addition, the data studied in the research model are from August 2012 to September 2018. The findings of the research showed that the intensity of the tendency of collec-tive behavior at the sector level is much stronger than at the market level. In addition, based on the findings of the research, it was determined that the distribution of simulation eigenvalues in three levels is significantly similar to the distribution of real data. Also, according to the investor's time horizon, the studied series always has a long-term memory for fluctuations. In addi-tion, it was found that long-term memory is directly related to fractal dimen-sions. The findings of this research, in addition to providing a new insight into the space-time correlations of financial markets, show that multi-level conglomeration is one of the mechanisms for creating the microscopic mi-crostructure of such markets. In other words, multi-level collective behavior is an important factor in the occurrence of cluster and fractal fluctuations in the market, and therefore, it should be considered from this point of view in the interpretation of the concept of risk and the definition of risk manage-ment strategies.

Companies pay attention to the value level of financial flexibility in making decisions related to optimizing investments and applying their net working capital policies. This issue will make profitable investment opportunities for companies more efficient and enable companies to gain more efficiency, as well as apply more optimal policies to keep cash. The purpose of this research is to investigate the effect of financial flexibility value and financial constraint on the speed of adjustment of net working capital, as well as the effect of financial constraint on the relationship between the value of financial flexibility and the speed of adjustment of net working capital in companies listed on the Tehran Stock Exchange. The appropriate pattern recognition test in combined data indicates the use of the regression model of the research using the panel data method with the fixed and random effects approach for the panel and pooled data patterns to estimate the regression model. The statistical sample includes 100 companies accepted to the Tehran Stock Exchange during the period from 2005 to 2020. The findings indicate that the value of financial flexibility has a positive and significant effect on the speed of adjustment of net working capital in the models of partial adjustments and error correction. Financial constraint has a positive and significant effect on the speed of net working capital adjustment, and it also has a positive and significant effect on the relationship between the value of financial flexibility and the speed of net working capital adjustment.

In the backdrop of an ever-evolving global business landscape and intense market competition, companies are faced with the imperative of strategically managing factors that influence their financial performance. This research delves into the intricate relationship between financial performance enhancement and corporate governance, with particular attention to the mediating role of human capital. The study centers its investigation on companies listed on the Tehran Stock Exchange and comprises a comprehensive sample of 140 top-level managers. A composite sampling approach, comprising a simple random sampling technique and Morgan's table, was employed to judiciously select a representative cohort of 103 participants. In the pursuit of rigorous academic analysis, the research leverages a goal-oriented, applied methodology, employing a descriptive survey design and a quantitative approach. The primary data for the study were methodically collected through rigorously designed and standardized questionnaires. Subsequent to data acquisition, a meticulous analytical process was undertaken using the Partial Least Squares (PLS) software, aligning with the latest developments in quantitative research techniques. The results stemming from hypothesis testing offer compelling insights into the dynamic relationship between corporate governance, human capital, and financial performance enhancement. Our findings convincingly demonstrate a significant positive impact of both corporate governance and human capital on the enhancement of financial performance in the context of Tehran Stock Exchange's listed companies. Furthermore, the empirical evidence strongly suggests that human capital plays a pivotal mediating role in the relationship between corporate governance practices and financial performance improvements. This study, in its pursuit of academic rigor, underscores the effectiveness of a novel hybrid approach, thoughtfully integrating machine learning and data envelopment analysis, to comprehensively examine the intricate interplay between financial performance enhancement and corporate governance within the context of the Tehran Stock Exchange's listed companies. The study contributes to the evolving body of literature in this domain and provides valuable insights for practitioners, policymakers, and researchers.

This study aimed to present an explanatory model of stock price using deep learning algorithm for companies listed in the Tehran Stock Exchange. In this study, a deep learning network was used to predict stock prices. The study was applied-developmental research in terms of purpose. To test the research questions, accounting data were prepared from 2011 to 2020 and input variables were calculated based on it for the model. The method of systematic elimination sampling has been used in this study. The results indicated that the precisions of prediction has a high precisions in the deep learning model. The proposed algorithm was reviewed according to its prediction accuracy and modeling cost. According to the data volume, it was found that the prediction accuracy in the deep learning model has a relative superiority and the diagram of performance characteristic and AUC criteria also showed this superiority in detection power.