محمدرضا رستمی

Meeting the needs of shareholders and investors, generating profit and income, and covering current expenses are the primary objectives of any economic entity. Banks and financial institutions are no exception, and calculating and understanding costs is one of their most critical activities. In today's competitive environment, organizations must adopt strategies that enhance the quality of services and products while reducing their costs. To achieve this, having tools for analyzing related costs and formulating cost policies for services and products is essential. Companies that previously used traditional costing systems have had to transition to newer, more dynamic, and flexible costing systems that account for various costs, including products, activities, distribution channels, and customers, reflecting the complexity of modern business and production processes. This article presents a model for calculating the cost of banking services using activity-based costing.

The research described in this article is developmental-applied. Given the various factors affecting costs and their nonlinear and cyclical effects, which render banks as complex systems, the methodology of system dynamics was used for modeling. This research is descriptive in terms of data collection methods, which included library studies reviewing sources and references on activity-based costing and systems dynamics principles, simulations concerning the cost of banking services using systems dynamics, and other sources such as books, magazines, articles, and theses from reputable libraries, information search centers, and credible websites.

 In this study, detailed models for two products, mudarabah and deposits, were developed. Cause-and-effect diagrams and their rates and statuses were drawn, and after testing the models, proposed scenarios were designed and compared. Four scenarios were suggested to improve system behavior: improving productivity, adjusting human resources, re-engineering business processes, and automating processes. To select the best policy—one that achieves the most significant cost reduction over a similar period—all policies were compared. The study's findings indicate that the policy of mechanization and process re-engineering is the most effective. Therefore, the bank's general policy for reducing product costs can be a combination of re-engineering and mechanizing processes.

Considering the dynamic business environment and banks as complex systems where many variables influence each other, the systems dynamics approach was employed to model the cost of banking services. The simulation results show that a combination of process re-engineering and automation can serve as an effective policy for reducing the cost of banking services.

Non-linearity feature and high fluctuations in financial time series have made the forecasting of stock prices and financial indicators face many challenges. However, recent developments in deep learning (DL) models with structures such as long-short-term memory (LSTM) and convolutional neural network (CNN) have made improvements in the analysis of this type of data. Another approach that can be effective in the analysis of financial time series is the decomposition of capital market signals through algorithms such as complete integrated empirical mode decomposition (CEEMD). Considering the importance of forecasting in the financial markets, in this research, by combining deep learning models and complete integrated empirical mode decomposition (CEEMD), The hybrid CEEMD-DL(LSTM) model has been used to forecast the Tehran Stock Exchange index. In this regard, the daily data of the total index of the Tehran Stock Exchange in the period of 2012/12/01 – 2022/02/20 be used and the results were compared with the results of competing models based on efficiency measurement criteria. Based on the obtained results, the introduced model (CEEMD-DL(LSTM)) has higher efficiency and accuracy in stock exchange index forecasting. Accordingly, the use of this model in financial forecasts is suggested.

Despite the increasing consumers’ environmental concerns, these concerns are not in line with their actual shopping behaviors. Therefore, to achieve sustainable development, we should focus on sustainable consumption instead of focusing solely on production. Relying on the qualitative research strategy, this study tries to design and validate substantial green consumer behavior using nudge theory. First, in-depth interviews were conducted with Iranian managers and professors who are active in green consumer behavior and green productions issues. The result was coded and categorized using the data theorizing method of the foundation in MAXQDA software. In the next stage, the questionnaire extracted from the paradigm model of research, design and distribution among consumers of green cosmetics in the country and 384 items were collected. Total 1321 basic codes, 46 concepts, 11 sub-categories were finalized. Through axile coding, 6 categories were identified including environmental attitudes, consumer innovativeness, personal norms, behavioral intentions, sustainable consumption behaviors, nudge marketing, contextual conditions, intervening conditions, and consequences. The result confirms the relationships between latent variables, confirming the research hypotheses and correctly identifying the components of the relevant model. The results of the study showed that the use of green nudges along to other factors can have a positive and significant effect on satisfaction, purchase of green products and conservation of natural resources without interfering with the right of consumers to choose products. The results can be effective on the strategies of managers and marketers in motivating customers to buy more green products and turning their attitudes into real purchases.

One of the most vital research programs of financial knowledge today, which is at the forefront of rejecting the theory of efficient markets, is financial behavior theory, which is the cause of joint efforts between financial sciences and social sciences and has deepened our knowledge of financial markets. Today's financial topics, which we are going to explain, describe and compare, are called financial behavior, which in simple terms is a pattern of thinking in which markets use patterns consisting of social sciences, psychology, finance and several other disciplines. They are studied, and in other words, economic agents are not rational in behavioral patterns contrary to neoclassical theories, but are normal either because of their preferences or because of cognitive biases. The purpose of this study is to design and explain a model for identifying behavioral factors affecting stock price fluctuations in the Tehran Stock Exchange. This research is applied in terms of purpose and in terms of working method is a type of survey research. The data of this research were analyzed using SmartPLS software. The results of the study showed that the emotional bias of short-sightedness, the emotional bias of optimism and the emotional bias of adaptation affect the fluctuations of stock prices of companies.

Rating of companies in the business environment is one of the most important tools for identifying internal strengths and weaknesses and external opportunities and threats of companies. The increasing expansion of new approaches for evaluating the performance and reporting of companies in developing countries, on the one hand, and the lack of a comprehensive model for rating companies admitted to the Tehran Stock Exchange, on the other hand, prompted researchers to create a practical model based on the integrated reporting approach for comprehensive evaluation and rating the listed companies on TSE. The model was designed using fuzzy Delphi technique, fuzzy Dimtel and improved fuzzy AHP technique consist of 5 criteria, 19 sub-criteria and 63 indicators. Results show, the financial performance is the most important and the environment is the least important criteria for determining the rating of companies, while the environment has the most and financial performance has the least influence on other criteria. Also, the top 10 indicators among 63 indicators make up a total of 49% of the weight of the model, and it is necessary for company managers to pay special attention to them in order to improve their ranking.

Financial markets play an influential role in economic development and most developed countries have more dynamic and stronger financial markets. Due to this market’s crucial role in the process of economic development, several studies have evaluated the role of financial markets in economy and Its effects on performance. These studies have often confirmed the positive impact of financial markets on the economic growth of countries. One of the most important components of financial markets is the stock market which in most countries, along with the banking sector, plays an effective role in financial resources allocation. Several factors can affect stock market returns. One of these is the government effectiveness, the regulatory quality, and the rule of law. Therefore, the aim of the present study is to study the role of government effectiveness, Regulatory quality and rule of law on stock returns in Iran and selected developing countries. For this purpose, the data of the period 2021-2000 and the quantile regression have been used.Based on the model estimation results, at a significant level of 5%, government effectiveness, regulatory quality and rule of law have a positive and significant effect on stock market returns. Also, the effects of inflation, per capita income, exchange rate and growth of industrial production on stock market returns have been positive and significant. In contrast, the impact of crude oil prices and interest rates on stock market returns is negative and is not statistically significant at the 5% level.

The purpose of the current study was the optimal allocation of assets using the cyclical behavior of commodities. For this purpose, the three commodities of copper, aluminum, and steel, along with gold and foreign exchange rate, were studied and their price data, along with the Tehran Stock Exchange (TSE) index during the period of January 2, 2009, to September 26, 2020, were analyzed. To examine the data, cluster analysis was used based on regime change behavior and Markovian state transition models were fitted to the data. Homogeneous and heterogeneous portfolios were constructed in terms of regime change behavior in both ascending and descending market regimes and the performance evaluation criteria of the portfolios were calculated. The results showed that portfolio diversification through the assets in the homogeneous cluster led to the best portfolio performance in the descending trend, while diversification by using commodity metals in the heterogeneous cluster might become the best performance in the ascending trend. In general, the results demonstrated that the use of commodities to diversify the portfolio led to better results in portfolio performance.

With the increasing popularity and widespread use of cryptocurrencies, the creation and development of methods for predicting price movements in this field has attracted a lot of attention. In between, recent developments in deep learning (DL) models with structures such as long-short-term memory (LSTM) and convolutional neural network (CNN) have made improvements in the analysis of this type of data. Another approach that can be effective in the analysis of cryptocurrencies time series is the decomposition through algorithms such as complete integrated empirical mode decomposition (CEEMD). Considering the importance of forecasting in the cryptocurrencies field, in this research, by combining deep learning models and complete integrated empirical mode decomposition (CEEMD), The hybrid CEEMD-DL(LSTM) model has been used to forecast the bitcoin return (as the most popular currency). In this regard, the daily data of the total index of the Tehran Stock Exchange was used in the period of 2013/01/01 – 2022/05/28 and the results obtained were compared with the results of competing models based on efficiency measurement criteria. Based on the obtained results, the use of the introduced model (CEEMD-DL(LSTM)) has increased the efficiency and accuracy of bitcoin return forecasting. Accordingly, the use of this model in this field is suggested.

Herd Behavior is one of the biases behavioral that affects financial markets and decision making. Herd Behavior in financial markets is the tendency of a group of investors to follow the actions of others and to ignore personal beliefs and information and following the market consensus. The purpose of this study is to investigate the relationship between Herd Behavior between crude oil prices and the stock market. In addition, the effect of variables such as upturns and downturns periods, investor tendencies and market liquidity on the relationship between the two variables will also be tested. This paper contributes to the debate on the cross-markets correlation by extending tests of herding behavior between the stock market and the crude oil market. We study herding behavior at the sector level during extreme oil market movements. Furthermore, we investigate the cross herding behavior between the two markets while including the volatility of the oil prices and investors’ sentiment. We use a modified version of the cross section absolute deviation model to infer the impact of the oil market on industry herding.

This research is an applied research in terms of purpose and a correlational research in terms of method. The research sample, after imposing the intended restrictions, included companies enlisted on the Tehran Stock Exchange, in the period from 2015 to 2020. To test the research hypotheses, a panel data model was used, and to measure the Herd Behavior extends the work of Chang & et al (2000). Then we tested the research hypotheses.

The research’s Results confirm the Herd Behavior in the Iranian stock market. The return of the crude oil market has an effect on the Herd Behavior  in the Tehran Stock Exchange. In addition, this behavioral biasess is greater during periods of crude oil market upturns than during periods of downturns. The results also showed that more liquidity in the market is associated with more Herd Behavir.

This paper examines herding behavior within Tehran Stock Exchange industries and explores the impact of oil prices on the intensity of herding. It further investigates the asymmetric effect of oil prices moves (downturns and upturns) on herding within sectors, while including both the volatility of the crude oil market and investors’ sentiment. The reason behind the inclusion of oil prices and volatility is to study the possible integration between the stock market and oil market and to infer the possible cross-herding behavior between them. There is herd behavior in the Tehran Stock Exchange and the crude oil market as one of the most important sectors of the economy and the largest source of government revenue also affects it. Therefore, the capital market and the effects it receives from the crude oil market should be considered more and more by economic decision makers.

Despite theoretical and empirical validity of capital asset pricing model, empirical evidence shows that investors ask Risk premium due to idiosyncratic volatility. Considering the idiosyncratic volatility in capital asset pricing، this study aims to Determine Firm characteristics which explain idiosyncratic volatility in Tehran Stock Exchange. In this study, we measured the idiosyncratic volatility, employing the 5 and 7 multi factor models and using the conditional and non-conditional methods. more over we investigate the relation between firms characteristics and idiosyncratic volatility by panel Regression. The research population contains the whole firms in Tehran Stock Exchange and OTC during 2011-2017, amongst them 82 firm were selected based on specified sampling conditions. Employing various Ivol measurements, our results show that book to market equity ratio, size، sales growth, asset growth and liquidity, are the Determinants of idiosyncratic volatility and firms with greater size, lower liquidity, lower sales growth, higher book to-market equity ratio, and lower Asset growth have less idiosyncratic volatility

The main objective of this study is to estimate and evaluate the performance of the dynamic realized conditioned autoregressive value at risk model (Realized-ES-CAViaR-Add-RV-SAV) in forecasting tail risk measures (VaR and ES). In this regard, daily as well as intraday (hourly) data of Tehran Stock Exchange Index in the period of 24/6/2014 – 2/2/2021are used. The results of the model are compared to the results of ES-CAViaR-SAV and ES-CAViaR-AS models to investigate the effect of incorporating the realized component to the model. Using backtesting tools such as Bin, POF, TUFF, CC, CCI, VRate tests, Lopez loss function (LL) (in VaR part) and McNeil and Frey test and ranking according to MCS method in The ES part, the efficiency of the models are examined. The results of this study indicate the efficiency of all three models in forecasting the tail risk measures. In addition, the results show that the use of realized criteria increases the tail risk forecasting efficiency.

The purpose of the present research is to design a seamless optimized risk-based liquidity management model. The statistical population of this research is in the special holdings of social security investment company (SHASTA), Due to the limited statistical population, the total statistical society including the company holdings in the period 1391-1396 was considered as an example. The method of collecting information is library and field. Data analysis was performed using the multivariate regression model presented in the study using Eviews software. The results of the hypothesis test showed that there is a significant relationship between risk sensitivity, price fluctuations, expected returns fluctuations, liquidity risk, and liquidity risk with liquidity management.

Many studies generally have represented the positive effects of investor relationship management on stock market value creation. The purpose of investor relations is gaining support and understanding via mutual benefit created between stockholders and organization that leads to an interactive and two-way collaboration relationship. Successful companies utilize the relationship marketing management with investors and stockholders as one of the strategies for utilizing sustainable competitive advantage in today's business world. This is the world in which maintaining the current stockholders to reach the goals is essential. In this study relation between active firms in Tehran Stock Exchange Market and its stockholders have been investigated based on the relationship marketing philosophy. In the next step, we have examined the effect of IR management on financial outcomes in the stock. We have adapted a framework proposed by Hofman and his teammate in 2010. A sample with some criteria extracted from The Tehran Stock Exchange database from 2011 to 2015. Multivariate linear regression model we are used within this research for hypothesis testing the cross-sectional data and compound method. The main dimensions in the model are investor relations, relationship marketing, relationship orientation, relationship evaluation, trust, commitment, liquidity stock return and cost of equity capital. Research findings show that there is a positive and meaningful relationship between liquidity and all dimensions of relationship marketing except for relationship evaluation. There is a positive and meaningful relationship between stock return and all dimensions of relationship marketing except for commitment. A positive and meaningful relationship was obtained for relation between reciprocity and cost of equity capital. No evidence was found and theories was not approved for meaningful effect of relationship orientation, relationship evaluation, commitment and trust on the cost of equity capital. As a final conclusion, due to importance of liquidity and stock return in this emerging market, it is beneficial for active companies to pursue dimensions of relationship marketing to manage their relation with the investors with quality.

Today, organizations follow specific financing, investment and liquidity policies with regard to their lifecycle stage. This study investigates firms' financing, investment and liquidity policies at different stages of their lifecycle. The population of this research is firms listed in the Tehran Stock Exchange during the years 2009 to 2015. The sample consists of 128 firms selected through the screening method and removal of outlier observations.This study determines the life cycle using accounting variables in order to examine its impact on capital expenditures, net of equity, debt and liquidity changes.This research uses panel data structure with fixed effect and multivariate regression model of analysis at confidence level of 90%. Findings show that investment is more in growth stages than of maturity and decline stages. Also, results show that there is a significant positive relation between the growth stage and debt issues

risk & return are two main factors that affect financial decisions. The trade off between risk & return create different investment strategies. In other words investment decisions are all based on risk & return. In this research we used multiperiod selection method in order to maximize investors utility. In this model we used not only variance but also higher order moment –skewness- for optimization.
For emprical test of the model we used return of first 50 companies stored by market capitalization in tehran stock exchange during 1386-1395. We used skewness
& transaction cost to introduce a moltipriod model in asset allocation to minimize variance of investors utility. Comparing the result of this model with markowitz model & simpel model considering investor preferences shows that based on performance evaluation criteria, the suggested model perform much better than the two other.

As banks’credit risk instability has a critical role on monetary and financial system, this study focuses on factors affecting both specific banking factors and macroeconomics factors on the credit risk of commercial banks in Iran. Based on Judgmental sampling method data of 9 independent and dependent variables was analyzed at the level of the commercial banks and the macroeconomics of the country by generalized method of moments (GMM) during the period from March 2009 to March 2016. The results show that the ratio of non-current facilities (NPL) of the past period, and the growth rate of GDP without oil with non-current facilities ratio (NPL) have a positive relationship with credit risk and bank's capital, the growth rate of oil revenue and credit growth, have a significant and negative relationship with credit risk. Also, bank size, inflation rate, and return on assets have a significant relationship with the credit risk criterion (NPL).

Predicting stock return rate is always one of important issues of financial markets and also major factor in investment. Predicting excess stock return based on debt-based data, other economic variables and capital market in Iran is the main purpose of this research. The proposed model emphasizes on nonlinear relationship between a set of quantitative variables and its structure basis is parametric and nonparametric models by using local kernel smoothing. First of all effective variables of debt return rate was investigated and identified. After identifying effective variables in first stage, prediction model for debt security return rate has been structured. This prediction is designed and presented by two methods of parametric and nonparametric prediction for debt security. In the following in order to determining effective factors of excess stock return rate prediction model structuring is investigated in the form of linear parametric and nonlinear nonparametric, by using results of first stage in three different steps and by observing R_v^2 criteria. The result of model implementation in all three examined steps and based on R_v^2 criteria show that results of various models of nonparametric approaches work much better than parametric approaches.

In the present study,we examined the effect of the expected investment, expected profitability and the book to market ratio on the market expected returns.
Fama-French model(2006) was used To calculate the Expected profitability and the expected investment. expected return, market value and book to market ratio was calculated by conventional methods. with the aim of increasing comparability, the two growth variable number of shares and equity book value growth also has been used separately. study was conducted in two stages regression so that the first stage regressions to estimate the expected investment and profitability expected and second stage regressions also is used to examine the relationships between variables.
The results show that there is a significant positive relationship between the expected return and the expected profitability and book to market ratio. The relationship between the expected return and the expected investment is significant negative. The results confirmed all the hypotheses.

With regard to the prominent role of mutual funds in financial markets, this study aims to examine the relationship between the mutual fund flows and the index of Tehran Stock Exchange (TEDPIX). For this purpose, the researchers used the daily data of 90 mutual funds during the period from March 2011 to March 2016 through "hidden co-integration approach and CECM. The study’s contribution is the implementation of hidden co-integration technique which­ enables researchers to identify the reaction­ of heterogeneous changes amongst two time series. Although, the results showed a hidden ­co-integration relationship between time series of mutual fund flows and TEDPIX, the findings of the study did not approve the standard co-integration relation amongst two time series. More specifically, there is a long-run relationship between positive components of flows and indexes, and another long-run relationship between the negative components and indices.

Financial market developments make it more important to measure market risks correctly. In this paper we investigatethe forecasting accuracy of different historical simulation models in relation to the risk measure expected shortfall and in comparison to established parametric models.we used historical simulation, mirrored historical simulatin,volatility weighted historical simulation,filtered historical simulation and GARCH(1,1) models.The data that we used consists of Tehran stock exchange market index from 2010 to 2014.Christofferson backtest used for value at risk and mc neil & frey backtest used for expected shortfall. According to unconditional coverage backtesting ,mirrored historical simulation model was rejected and others were accepted and according to independence backtesting all models were accepted thus the christoferson backtest will omit the mirrored historical simulation model and According to mc neil and frey backtest all models were accepted and finally the model confidence set procedure showed that semi parametric models are best models to forecast expected shortfall.

Financial researchers use different techniques to asses risk and explore its relationship with returns. Different results are why the relationship is known as “returns-idiosyncratic risk puzzle.” Statistical population consists of firms which are active in Tehran stock exchange. Time duration starts from 3 August 2013 and ends in 1 January 2017 This research uses panel-GARCH model to estimate idiosyncratic risk and then risk-returns relationship is studied based on quantile regression and Bayesian approach. The study shows that the relationship in lower quantiles is negative, in upper quantiles is positive and in median no relation observed. Therefore, results show that the relationship is nonlinear and based on returns distribution. This findings show that information that exists in distribution boundaries are important for financial data .Also, it is necessary to be captured by modeling and to be considered in interpreting the results. In addition, returns-idiosyncratic risk puzzle is solved.