alireza saranj

Oil price is often regarded as an important factor in analyzing variation in stock prices, but empirical evidence is mixed. This research is set to reconcile this conflict on the effect of oil prices on Tehran Exchange stock price. The nonlinear response of Tehran Exchange price index to oil shocks has been explained by a Markov Switching regime model. This research also uses a Structural Vector Autoregressive model to separate oil price shocks into three types in a time period from from 10/10/2010 to 10/10/2021 to test the hypotheses. The resualts show that oil price shocks have asymmetric effect on Tehran Stock prices. This asymmetry can be explained by sign of oil price shift and finally, response of stock prices to oil supply shock, global demand shock and oil-specific demand shock is asymmetric. During an increase in oil prices, it will be more likely to have large positive effects on stock prices than a decrease in price. Duration of the price index response in low regime is significantly longer than high regime. In both regimes, response of the stock price to the increase in oil prices is positive, but in the high regime, the magnitude and duration of response are greater.

The significant share of loans in the asset portfolio of banks has turned credit risk into one of the most important risks in the banking industry. Considering the difference in the structure, profit motive and risk management in different banks, the purpose of this research is to compare the factors affecting Non-Performing Loans (NPLs) as an indicator of credit risk in different banking groups. For this purpose, the relationship between Non-Performing Loans and explanatory variables (macroeconomic factors and bank-specific factors) was estimated for different banking groups by using the generalized method of moments (GMM) of dynamic panel data for the annual data of 1385-1400. Based on the results, bank-specific factors have a more effective role in increasing the non-performing loans of banks (especially state-owned banks) than macroeconomic factors, which is due to mechanisms that lack efficiency and effectiveness in the processes of granting credit and collecting bank claims; In addition, the influence of these factors is different in different banking groups.

Since financial developments that are not driven by fundamental factors of monetary policy may have a large impact on the economy, policymakers have placed significant emphasis on the financial conditions index over the past recent years. Policymakers must maintain a vigilant watch over financial conditions, as their significance becomes increasingly significant. The construction and use of the financial condition index include three issues, which are: a) the selection of financial variables to enter the financial index, b) weights used to relate financial variables to the index, and finally c) the relationship between this index and the macroeconomy. There are many reasons for the changeability of these three cases over time, which can be discussed about the reasons for their occurrence and effect on the results. Many changes affect the way a financial index is made. Therefore, in this research, the goal is to design an index of financial conditions using time-varying dynamic models to improve the forecasting of macroeconomic variables.

The process of implementing the conceptual model can be explained in the following steps. First, extracting the desired variables to be used in the desired models (for this purpose, we have used monthly data during the years 2001 to 2021 for 19 financial variables and 5 macroeconomic variables). It should be noted that since all the variables must be in the form of rates - if necessary, all the variables were converted into growth rates. Also, all the variables were examined from the dimension of stationary, and the problem of their stationary has been solved. Second, the desired models were calculated to predict macroeconomic variables. Using time-varying models, dynamic averaging models, and dynamic selection models, the financial condition index was constructed in such a way that this index could include various variables to adopt different coefficients from its previous and subsequent periods and challenge constant parameter and variable models. Third, the predictive power of each of the proposed models in the estimation of macroeconomic variables (sum of squares of prediction error) was analyzed and estimated. Fourth, financial index extraction was done based on the model selected in the third step.

The findings suggest that employing models that solely consider variability in the coefficients (without accounting for variability in the variables) leads to enhanced predictions of the unemployment rate compared to vector autoregression models and vector autoregression models incorporating the basic component. In addition, by moving from the generalized factor vector autoregression models and the time-varying generalized factor vector autoregression and the generalized factor time-varying vector autoregression, in which only the variability in the parameters is included (without considering the variability in the variables) towards the models that consider the variability in the parameters both in a Bayesian and dynamic way, a reduction will occur in the forecast error in the variables of the consumer price index, liquidity, monetary base, and gross domestic product. However, the amount of improvement in each of the variables is different from each other.

This study employed time-varying models to derive the financial condition index, aiming to provide the most accurate estimation of macroeconomic variables. The findings demonstrate that the utilization of these models outperforms other approaches in forecasting a majority of the macroeconomic variables

Real estate is an important asset to individuals and organizations. In addition, the large number of jobs involved in this sector, along with the volume of working capital, has led to it being referred to as an economic engine. Research has shown that new challenges in the industry have increased competition and the need to pay more attention to strategic management concepts. The purpose of this study was to identify specific business strategies in the iran real estate industry and also to explain the core competencies required in the industry in relation to these strategies. In this research, qualitative research method and inductive logic were used to reach the part from the whole and the sample size was expanded until the data saturation was reached. Finally, the research reached saturation level with 23 samples of interviews with experts with at least ten years of useful work experience in the industry. It should be noted that in order to evaluate the questionnaire, the CVR index was used and in order to evaluate the quality of the research, the Creswell approach was used. Finally, using content analysis extracted from the data, eight industry-specific strategies and three core competencies were introduced in relation to these strategies, and the relationship between the need for each of the competencies and identified strategies was presented.

Investigating the probablility of rare events occurring (events that occur with very low probability) is an important issue in portfolio risk management. extreme value theory of value provides the mathematical basis for modeling these events and calculating the risk criteria associated with them, such as the value at risk. The purpose of this paper is to model the dependency structure andextreme value theory of 10 foreign exchange companies of Tehran Stock Exchange (Persian Gulf Holding, Bandar Abbas Refinery, Mobarakeh Steel, Topico, Ghadir, Parsian Oil and Gas, Melli Mes, Gol Gohar, Mobile Communications, Chadormelo).The results indicated the fact that among the stock returns of the top 10 companies evaluated, it is possible that using extreme value theory of value using vine Copula functions, the results of the forecast were greatly increased. Results of the copula function in six modes: simple Copula (t), time-varying Copula (tDCC), and Gaussian distribution-based time-varying Copula (GDCC). tvSJC) was investigated. In all six cases, the use of the Copula -wine method increased the accuracy in predicting optimal stock returns.

The capital asset pricing model provides an equilibrium model to show the relationship between risk and return on assets. One of the economic areas is herd behavior, which has attracted a lot of attention in recent decades. Therefore, the present study deals with the herd behavior in the Iranian economy on the efficiency criteria of the asset pricing model. The research method used in this research is correlational research that a regression method has been used to test the research questions. To test the hypotheses, Karhart's 4-factor model was used, to which the bulking factor was added. The statistical sample of the present study includes 115 companies active in the Iranian economy. The research period is for 10 years, which has been studied from 1389 to 1398. The results show that mass rotation is different in different degrees of risk and occurs more in high risk areas of the market and causes a return of beta in the market and the inefficiency of the capital asset pricing model to is following. It was also found that by removing high-risk portfolios from the sample, no bulkiness is observed and beta behavior is stabilized according to the theory of capital asset pricing model and beta return does not occur.

Investor sentiment is one of the non-fundamental factors that affect the financial markets, which itself is influenced by various factors, including oil price changes. This study aims to investigate the impact of oil price on investor sentiment in stock market industries in the Tehran Stock Exchange (TSE) using monthly data from April 2010 to June 2020. To investigate this issue, stock exchange industries were grouped into three categories: total industries, oil-related industries, and non-oil industries, and the effect of oil prices on investor sentiments in these three groups was examined using the pooled mean group (PMG) technique. The PMG approach considers both the short- and long-run relation between series and provides reliable results in the context of dynamic heterogeneous panel models. The implementation of PMG in all three models shows the impact of oil prices on investor sentiment over both the short and long run. Findings suggest also that oil price has positive and significant in all three models in the long run and the oil price coefficient is higher in oil-related industries than non-oil-related industries. These results are the opposite of the results obtained by similar studies, which can be due to the special features of countries, e.g. being oil exporters or oil importers

Banks and financial institutions strive to develop and improve their credit risk evaluation methods to reduce financial loss resulting from borrowers’ financial default. Although in previous studies, many variables obtained from financial statements – such as financial ratios – have been used as the input to the bankruptcy prediction process, seldom a machine learning method based on computing intelligence has been applied to select the most critical of them. In this research, the data from companies that are were listed in Tehran’s Stock Exchange and OTC market during 26 years since 1992 to 2017 has been investigated, with 218 companies selected as the study sample. The ant colony optimization algorithm with k-nearest neighbor has been used to feature the selection and classification of the companies. In this study, the problem of the imbalanced dataset has been solved with the under-sampling technique. The results have shown that variables such as EBIT to total sales, equity ratio, current ratio, cash ratio, and debt ratio are the most effective factors in predicting the health status of companies. The accuracy of final research model is estimated that the bankruptcy prediction ranges between 75.5% to 78.7% for the training and testing sample.

A great amount of potential financial losses arise from borrowers’ abstaining from refunding their debts calls and the development and improvement of credit risk measurement techniques in the financial literature in order to decrease such losses has transformed into an intevitable subject. The purpose of bankruptcy forecasting models is to estimate the probability of a company or a person’s abstaining during a certain period of time. This research used the data gathered from a sample of 218 active companies in Tehran Stock Exchange Market as well as Over-The-Counter for the period between 1990 and 2016. Moreover, ants’ colony algorithm was used to determine the most effective factors of credit risk and also pattern recognition neural network technique was applied to classify and evaluate the precision of bankruptcy forecasts. As a result, such ratios as profit before interests and taxes to total sale; total benefits of shareholders to debts; and current ratio, cash ratio and shareholders’ benefits ratio to total assets are the most effective factors. Finally, the presented model which employs data belonging to one, two and three years before the intended year is able to forecast the credit condition of companies with higher precision as compared to the average precision of current models.

Stock trading systems development using evolutionary algorithms over the past few years has become a hot topic in financial fields. In this paper, an intelligent technical trading system was proposed using a combination of MLP neural network and evolutionary algorithms (i.e., GA, ACOR, and PSO). In order to select the final variables as the selected features, a return comparison of each indicator ratings was used based on tradings. Finally, the performance of each model is tested in comparison with the buy and hold strategy. The results show that the evolutionary learning algorithms significantly outperform the benchmark models in terms of the average return and the hybrid MLP_PSO model outperforms others.

Statistical arbitrage is a common investing strategy in inefficient markets which is market neutral and profits from both sides of the market without the need for initial capital. This research aims at designing suitable models for stock statistical arbitrage using deep neural network, random forest, gradient-boosted trees and equal-weighted ensemble of these methods whilst analyzes the returns and risks of the designed models. For this purpose, the information of all listed companies in Tehran Stock Exchange from 1385 until 1396 has been used to generate trading signals. The design of the research models and required coding also the testing of the research hypotheses which is analyzed by t-test were performed in R software. The research findings show that the highest daily return is 4.24% for k = 5 (prior transaction costs) which is for the simple equal-weighted ensemble (ENS). Also deep neural network (DNN) has the lowest value at risk (- 4.45%) and the lowest expected shortfall (- 5.57%) for k = 20. The highest value of the return to standard deviation ratio is 1.072 which belongs to the RAF model for k = 20. Moreover, research results show that recent returns have higher predictive power than previous returns.

The return volatility in financial markets depends heavily on the nature, behavior and desires of the trader. The aim of this study is to determine the appropriate trading strategy in Iran stock market based on the behavior of noise traders. In order to achieve this research aims, related data to total index and 96 selected companies has been used. The results of daily fluctuation analysis of the total index by using the Chow-analysis multiple variance test (CD) during the five-year period from 2011 to 2016 showed that the common culture of traders on Iranian stock market is noise trading. Statistically significant Behavioral Error (BE) of Iranian stock market traders point out that this market faces the noise trader risk (NTR). So the results of the survey of the effect of noise trader risk (NTR) lagging value on the stocks retune of the selected companies by using a two-variable regression model indicate that: firstly, noise traders have a significant effect on stock returns in this market, and secondly, the systematic noise effect (SNE) overcomes cash noise effect (CNE), That means that noise traders by adopting a noisy strategy and expanding their risk exposure on market can earn more than information traders with reverse strategy. The results of this research can help investors to adopt a suitable trading strategy and competent authorities to apply mechanisms to reduce noisy transactions.

In this paper, I present an approach for valuing credit default swap (CDS), tranches of synthetic collateralized debt obligations and kth to default swaps. One-factor gaussian copula model is utilized to model default correlation for each pair of companies. In this research, I analyze the effect of different hazard rates and different default correlation between each pair of names on the spread to buy protection for multi-name credit products. The findings in the valuation of a tranche of a CDO show that if the correlation is low, the junior equity tranche is very risky and the senior tranches are very safe. As the default correlation increases, the junior tranches become less risky and the senior tranches become more risky. The valuation of kth to default swaps also shows that as the hazard rate increases, the spread of all swaps increases. Also, increasing the correlations between all firms while holding the hazard rate constant lowers the cost of protection in kth to default CDS if k is small and increases it if k is large.

investigates the existence of noise traders in Iran stock market and tests a quantify model for measuring noise trader risk, overreaction, underreaction and mispricing. For the first time, the most viewed stocks were used to construct a behavioral index. This new index can be used to identify noise traders and applying this index can be obtained more accurate estimate of the beta in compared with market index. Also by using CAPM and BAPM models during the period from 2011 to 2016 for 96 companies, it was shown that Iran stock market has a significant behavior error. Furthermore, the results of information-adjusted noise model (IANM) show that noise traders are active 100 per cent of the time on the Iran stock market and make it inefficient. The most type of inefficiency in this market is overreaction in 46.67 percent of the time and then information pricing errors and under reaction are in 45.63 percent and 7.71 percent of the time, respectively. The findings of this study are useful for understanding the market atmosphere and new behavioral index can be used as a proxy of Iranian investors` sentiment.

Stress testing is a simulation technique to evaluate portfolio reactions to several critical situations. In this paper, we review different stress testing methodologies to examine impacts of different stress scenarios on an Iranian equity portfolio. We identify the extreme tails of all risk factors in our portfolio by extreme value theory and model their dynamic and nonlinear dependence structures with copula functions. We performed three stress tests such as historical, hybrid and hypothetical stress scenarios to simulate the joint evolution of risk factors over time in a realistic way. According to the empirical findings, we find that historical scenario method is not a suitable tool for stress testing due to several drawbacks and show the importance of forward-looking analysis such as hybrid and hypothetical scenarios. We also indicate that the hypothetical stress approach is superior to the other two scenarios from the perspective of stress testing.

One of the main concerns of the market regulators is the prediction of the effects of these new strategies on the market due to the heterogeneity of the agents, rational boundary and behavioral factors in the investors’decision making. The Iranian stock market has always been fluctuating; therefore,awareness of the effects of strategies before they are implementedwill help regulators to market more effectively. The main objective of this research is to create an artificial market according to the Iranian stock market so that different scenarios can be simulated. One of these emerging areas, which emphasizes the impact of social sciences, cognitive sciences and behavioral sciences on operational research, is "Behavioral Operations Research" that helps us solve real-world problems.In this research, considering modeling based onagent-based capabilities, shareholders’ capabilities, bonds including different types of stocks and risk-free papers, and trading rules. In this artificial market in each trading period, in accordance with the trading strategy and learning procedures, the agents intendto buy and sell. Eventually they worked as the market makers, in accordance with the auction mechanism, and began to execute orders and perform clearing and settlement operations. In order to examine the validity of the model, the statistical output of this market wasadapted to the statistical characteristics of the financial markets and, after validating the model with the scenario, simulation of the research questions were done. In this research, the scenarios for eliminating the range of price fluctuations and elimination of the informed stakeholders and their effects on stock prices were reviewed. According to the simulated scenarios of the Iranian stock market, due to its immature nature, eliminating controlling mechanisms such as the range of price fluctuations, in the short term the Market willbe highly instable, but in the long run the market tends to be more efficient.

Understanding and the investigating the behavior of stock prices, has always been one of the major topics of interest to the investors and finance scholars. In recent years, various models for prediction using neural network and hybrid models have been proposed which have a better performance than the traditional models. Here a hybrid model of neural network and wavelet transform is proposed in which genetic algorithm has been used to improve the performance of wavelet transform in optimizing the wavelet function. Daily stock exchange rates of TSE from April 21, 2012 to April 19, 2017 are used to develop a prediction model. The results show that it is possible to find a wavelet basis, which will be appropriate to the intrinsic characteristics of time series for prediction and the prediction error in this model is reduced comparing to the neural network and hybrid neural network and wavelet models.

In this paper, we used Iranian stock market data (TEPIX) to identify the bull and bear phases and to analyze their characteristics during the period of 1991-2017 using a non-parametric approach. Having determined bull and bear phases, we calculated the following five indices (durations, amplitudes, cumulative movements, excess movements and ratio of big expansions and contractions) using a non-parametric approach. The results showed that there are some common facts about the cycles that average duration and amplitudes of the bull market are longer than that of the bear market which are also true in Iran Stock Market. However, the excess index of the bull market is not larger than that of the bear market in Iran. We also found that bull phases are longer and more intense (larger amplitude) than bear phases and the rate of the growth index in the bull periods is higher than the rate of its slowdown in bear periods

This paper proposes a method based on rough set theory and by using technical indicators to predict the stock price. Rough set theory has several advantages; the most important advantage is that no additional information is needed about the initial data. In the proposed model, a number of technical indicators from the data of Bank Saderat Iran during a year were calculated and used as condition attributes in the decision table and the stock price fluctuation on the next day was selected as decision attribute. It should be noted that by using the correlation matrix analysis, the variables with the highest correlation with decision attribute were selected as conditional attributes. Using rough set theory and different discretization and reduction methods, some rules are extracted based on learning data and methods validity were computed based on control data. Comparing the return of this method and buy and hold method reveals the superiority of proposed model. Also, using data from different years with different price trends as inputs to the model and achieving satisfactory results is a promising reason for using and developing this method in stock price forecasting.

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.

In this paper, we deal with the ranking of different VaR and ES approaches using daily banking industry index data over the period 2008 to 2016, with an emphasis on Conditional Extreme Value approach. In the first stage, we use Bernoulli coverage and independence of violation tests for VaR models and McNeil & Frey’s backtest for ES models to examine the validity of these models. In the second stage, we import the loss functions of the valid models remained from the first stage into the MCS function and rank statistically them. The loss function used for VaR models is Dowd loss function and the one used for ES models is Olsen loss function. The results show that the in both VaR and ES models, the conditional EV with normal standardized residuals, the conditional EV with student's t standardized residuals and GARCH with student's t residuals models are respectively ranked first to third.

This paper investigates the relative performance of Value-at-Risk (VaR) and expected shortfall (ES) models using daily overall index data from TSE for a period of 8 years from 2008 to 2016. The main emphasis of the study has been given to Conditional Extreme Value Theory (CEVT) and to evaluate how well Conditional EVT model performs in modeling tails of distributions and in estimating and forecasting VaR and ES measures. We also compare them with parametric approaches. We have compared the accuracy of Conditional EVT approach to VaR and ES estimation with other competing models. We use Bernoulli coverage and Independence of violation tests for backtesting the VaR models and McNeil & Frey’s Backtest and Model Confidence Set to assess the performance of the ES models. The best performing VaR and ES models is found to be the Conditional EVT. MCS function result for ES also shows that the Conditional EV with student's t standardized residuals, Conditional EV with normal standardized residuals and GARCH with student's t residuals models are respectively ranked first to third.

This paper examines regime shifts in TEDPIX return and volatility and the effects of positive and negative crude oil shocks and gold price fluctuations on stock market shifts behavior using Markov switching EGARCH model with Student’s t-distribution. We detect two episodes of series behavior، one relative to low mean/high variance regime namely bear state and the other to high mean/low variance regime namely bull state so that the expected duration of bull state is more than twice as long as the other one. The results show that the external variables involving positive and negative crude oil shocks and gold price fluctuations significantly impact neither on the equity return nor on transition probability between regimes but they have significant effect on the stock market volatility.

In recent years, as one of the most important efforts made in order for the partnership bonds to get more diversified and competent, they have focusedon making these bonds equipped with a set of the other financial tool's features. This issue is the present article's subject matter. Given the partnership bond's role in financing the investment projects in Iran, increasing these bond's capabilities and diversity can even increase the amount of financial resources in hand for being invested. In this connection, our question here is that whether one can equip the partnership bonds with a set of stock market's features and, meanwhile, cover the risk of exchange rate fluctuations. In response, we have introduced "convertible currency partnership bonds". These bonds not only give the holders the benefits of stocks and participation bonds, but also keep their assets secure against the risk of exchange rate fluctuations. The method used here is a descriptiveanalytical one.

Behavioral finance is a new field in financial theories that seeks to explain stock market anomalies by putting aside the assumptions of modern financial theories about rationality of the economic agents and efficiency of the markets. The effect of the momentum that is the continuation of price trend in medium term is one of these anomalies. To explain this subject, as stated above, there have been many investigations conducted on behavioral finance and the related influencing variables. The present article investigates the relationship between the momentum return and liquidity at the Tehran stock exchange. The findings of the study reveal that there is no specific pattern to affirm