mohammad ebrahim aghababaei

In the past few years, several major domestics and international challenges have emerged, causing global political and economic uncertainty. Economic uncertainty, defined as the difficulty in predicting the economic environment, arises from various factors such as political instability, changes and uncertainties in government policies, natural disasters, and market fluctuations. The presence of such uncertainties significantly affects the efficiency of markets, including the efficiency of the capital market. The aim of this study is to examine the long-term relationship between of economic policies uncertainty (based on the fluctuations of macroeconomic variables using the composite PCA index) and the excess return of eight different industries index (Automobile and parts manufacturing, Pharmaceutical Products and Materials , cement, lime and gypsum, Multidisciplinary industrial companies , basic metals, oil, coke, and nuclear fuel, coke and nuclear fuels, chemical products, Aggregation, properties and real estate).The investigation, conducted using the econometric ARDL approach over the period from 2012 to 2021, demonstrates that economic policies uncertainty is positively and significantly related to the excess returns of the selected industry index Among the various industries, the Automobile and manufacturing parts industry is most affected by the of economic policies uncertainty, while the construction and real estate industry is least affected. Furthermore, the speed of adjustment of Aggregation, properties and real estate the effect of Economic policy uncertainty on the excess returns of the stock market industries is not homogeneous, as indicated by the ECM coefficient. Automobile and manufacturing parts industry index experiences the fastest adjustment, while the Multidisciplinary industrial companies, due to their diverse portfolios, exhibit the slowest adjustment speed compared to others..

Individual decisions in the new paradigm of financial science are influenced by emotional factors. These factors can also have an impact on financial markets. First, using the Baker and Wurgler model (2007) to model the Iranian capital market sentiment index, and the capital market is divided into two periods of low and high sentiment. In order to examine the relationship between cross-sectional return and value-at-risk (VaR) in periods of high and low sentiment, two approaches were used: Fama-Macbeth regression model and single-sorting and double-sorting. The statistical population of this study consists of 130 companies represented on the Tehran Stock Exchange, selected using a systematic elimination method. MATLAB software was used to implement the method. It was found that, regarding the sentiment factor, there is a significant relationship between VaR and excess return, such that VaR changes were greater in periods of high sentiment than those in the periods of low sentiments. During periods of high sentiment, shareholders’ transactions are determined solely by emotions and with high risk. Furthermore, the relationship between excess return and VaR was only influenced by momentum

The bank credit system is limited and the bond issuance process is complex. Most firms issue new shares to raise the funds they need to develop their activities and implement their plans.This article details an investigation of the impact of investor sentiment and using market timing at the time of SEO, the short and long-run stock price performance subsequent to SEOs and the cross-sectional and time-series effects of investor sentiments on both short and long-run stock price performance subsequent to SEO events.We used the Baker and Wurgler(2007) model to estimate the sentiment index in the TSE. Next We categorize all sample SEOs into high and low sentiment, according to the level of the sentiment index in the month when the SEO is conducted. we use cumulative abnormal-returns(CARs) to investigate short-run stock price performance and the buy-and-hold returns (BHAR) to examine long-run stock price performance. The data were compiled of 101 firms from different industries of TSE in the period of 1391-1398. Firms conducting SEOs during high sentiment periods experience less severe short-run price drops around the issuance and this effect are stronger for small, young, and high market-to-book ratio firms. firms conducting SEOs during high sentiment periods experience more severe post-issue long-run underperformance and this effect of investor sentiment are stronger for big, older, and low market-to-book ratio firms. By considering sentiment index level around SEOs and the firm's characteristics, investors can adopt the investment policy of selling,-buying or holding shares after the SEO.

The purpose of this research was to investigate the effects of financial restatement on various investment inefficiencies with emphasis on the role of financial constraints. In this study, Richardson (2006)'s model of investing expectations was used to measure investment inefficiencies and categorize the sample member firms into over-invested or under-invested corporates. Also, the index defined by Kaplan-Zingales (1997) was utilized to examine the financial constraints. The sample included 174 companies that were active over 6 years (2012-2017) in the Tehran Stock Exchange. The results indicated that financial restatement could reduce the over-investment, while it exacerbated the under-investment ones. On the other hand, the companies with over-investment might experience greater financing constraints after financial restatement. This relationship was not significant for under-invested companies. Finally, the argument that financial restatement could affect investment inefficiencies by indirectly affecting financing constraints was also not approved by any groups of the compaines.

Dividends are recognized as an important and influential factor in the value of stocks and therefore the market value of the company. It can be said that dividend (cash) has always been of great importance for investors and shareholders. Therefore, in the research, the effect of dividend policy on firm value has been investigated by considering the basic principles of the company and also the effect of company size on this relationship. Finally, the amount of dividends premium in this period is stated.Research In order to achieve the goal of research, 93 companies listed on the Tehran Stock Exchange during a period of 8 years (2013-2020) 744 years-company has been done using the panel data regression method. The relationship between dividend payout and the firm value is positive and significant. As a result, the dividend premium in this period is also positive. Panel data regressions suggest that the dividend premium for firms’ equity and the dividend premium for firms’ assets. Dividend premium in this period is also positive. According to the results, the stock prices of dividend payers are great compared to those of non-payers (retained earnings). The stock price with dividend payout is higher than the shares with accumulated dividend. The positive dividend premium indicates the investors' preference for dividend payout

Behavioral finance and market microstructure studies showed that investor sentiment has a positive and meaningful effect on liquidity. The purpose of this study is to investigate the effect of the Aggregate Sentiment Index (ASI) on liquidity and its volatilities and to examine the symmetry of news (shocks) on the Tehran Stock Exchange (TSE).

The available academic literature on liquidity proposes a wide set of indices and proxies to measure the different characteristics and dimensions of liquidity. In this study, four indices were employed to measure the various dimensions of liquidity including Transaction Cost, Volume-Based Measure (Trading Volume), and price-impact characteristics. Accordingly, the indices of turnover and trading volume were related to the transaction costs dimension, the Amihud illiquidity ratio was related to Volume-Based Measure (Trading Volume), and the High-Low Spread index was related to the price impact characteristic. Irrational Aggregate Sentiment Index (ASI) was formed using nine indirect (institutional) indices for measuring investor sentiment using Principal Component Analysis (PCA) and the GARCH model was used to investigate asymmetry. The E-GARCH and T-GARCH models were also employed to investigate the asymmetry of news (shocks).

The achieved results proved no significant relation between Turnover and Trading Volume indices but the strong impact of Amihud illiquidity ratio on investor sentiment. The findings also indicated the effects of shocks as asymmetric, i.e., optimistic (positive) shocks have greater effects than pessimistic (negative) shocks. Positive shocks cause a decrease in volatilities and an increase in liquidity.

Since Amihud’s illiquidity ratio is mostly affected by investor sentiment, policymakers can increase liquidity and decrease price volatility by controlling investor sentiment.

Investor sentiment about the capital market can play an important role in stock price trends, market transactions, and especially on the stock return synchronicity. The entry of many Individual investors who do not have enough information about investment, the study of this issue has made more important. To this end, in this research we investigate the relationship between investor sentiment and stock return synchronicity in Tehran Stock Exchange, by using the financial data of 167 firms listed on Tehran Stock Exchange from 15 various industries during ten years from March 2010 to March 2020. We use the Baker-Wurgler (2006) sentiment index as our primary measure of investor sentiment that Measured by principal component analysis. Also, three different methods have been used to measure the stock return synchronicity: the Mork model, the Carhart four-factor model and the Fama and French five-factor model. The results show that the Investors sentiment significantly affect on increasing stock return synchronicity. The findings also showed that the coefficients of positive and negative sentiment are not significantly different and as a result, positive and negative sentiment are symmetrically affecting the increase in stock return synchronicity.

Today, one of the major problems of banks is the amount of their non-performing loans. The ratio of non-performing loans is used as a measure of bank health. Therefore, the purpose of this study is to investigate the factors affecting the ratio of non-performing loans. In this regard, the vector Auto Regressive– vector error correction model has been used to examine the effects of macroeconomic and financial variables such as business cycle, government debt, unemployment rate, foreign direct investment, and domestic credit by the banking sector. To achieve the research goal, quarterly data from 1996 to 2017 were collected from Iranian banks. According to the results of the study, the relationship between business cycle, government debt, and foreign direct investment and the ratio of non-performing loans is negative, while he relationship between domestic credit provided by the banking sector and non-performing loans ratio is positive. Unlike other variables, the unemployment rate variable has a short-term effect on the ratio of non-performing loans. According to the results of analysis of variance of the estimated model in the long run, approximately 48.96% of the changes in the ratio of non-performing loans, through the past values ​​of this variable, 41.42% by the government variable, 4.14% by the banking sector domestic credit variable, 3.12% by the unemployment rate variable. 1.3% is explained by the business cycle variable and 1.6% by the foreign direct investment variable.

One of the factors affecting stock market returns is the change in oil return and volatility that can help explain the returns and adopt profitable strategies. Considering the importance of the impact of the crude oil price changes on stock return in different industries, this study uses monthly data from 2009 to 2018 to determine whether oil return volatility affect the profitability of Tehran stock exchange strategy? Besides of robustness of the results, the efficiency of the momentum strategy based on crude oil price data is compared with the efficiency of the conventional momentum strategy. The results show that the conditional oil return volatility and the stock market return are able to explain the momentum return of industries, the portfolio return of the winner industries and the portfolio return of loser industries. The study shows that the results are robust and the oil return volatility after controlling for macroeconomic variables has high explanatory power. The momentum strategy also yields higher returns than oil-based momentum strategy.

The purpose of this study is to investigate the presence of under-reaction or over-reaction in Tehran Stock Exchange after extreme market events. Regarding the vast number of these events, finding how investors trade in these conditions is crucial for timely decision making and portfolio management. Regulations also can be wisely made to control investors.

Using daily TEDPIX and daily prices of all stocks from 2009 – 2018, we identify extreme market events by comparing daily market returns with the 500- days moving Value at Risk (VaR) of returns. Winner and loser portfolios are formed in the second stage and Then, we investigate the presence or absence of under-reaction or over-reaction by comparing momentum and reversal strategies' excess return. Positive values of momentum strategy excess returns lead us to under-reaction and positive values of reversal strategy lead us to an over-reaction.

According to the results from the present study, from 67 extreme events in the research period, we have 30 positive and 37 negative extreme events. The momentum strategy yields a positive and significant excess return on average. Consequently, we find that stocks tend to under-react after both positive and negative events. But this behavior is more intense after negative events. Thus, the momentum strategy can make excess returns. Also, we didn't find the persistence of over-reaction within the study criteria.

Short-term under-reaction in the Tehran Stock Exchange may be due to the daily price limit of this market. Disregarding of the cause, investors can make a profit by taking advantage of the opportunity in the days following an extreme event. In other words, investors that prefer a momentum strategy can have notable excess returns in the days following an extreme event.

Investors often aim to reduce the risks associated with traditional assets such as investing in equities. The risk of stock price volatility is one of the concerns that always active investors are trying to manage it. Therefore, this research tries to investigate the effectiveness of the hedging stock price volatility methods and its diversification using the gold coin futures. In this study, using monthly data on stock index returns and gold coin future returns between 2008 and 2018, we compare the result’s effectiveness of dynamic hedge ratios using multivariate generalized auto- regressive heteroskedasticity (BEKK-GARCH) and recursive ordinary least square (OLS) methods. The results indicate that the use of the gold coin future is suitable instrument to hedging stock portfolio and the multivariate GARCH method is more effective than the rolling regression method in 5 conditional effectiveness indices, including percentage reduction in variance, percentage reduction in semi-variance, percentage reduction in hedged losses, percentage increase in portfolio excess returns and percentage increase in positive returns.

In the present era, businesses have developed to a large extent which has, in turn, forced them to manage their resources and expenditures wisely for the sake of competition. This is mainly because the competitive market has severely reduced the flexibility of companies, which means that their ability respond to different economic situations has reduced and this puts most firms at the constant risk of bankruptcy and contraction. Therefore, in this study, we have tried to predict the bankruptcy of manufacturing companies through preventing the occurrence of such risks. 

In this study, the "Kernel Extreme Learning Machine" has been used as one of the artificial intelligence models for predicting bankruptcy. Given that machine learning methods require an optimization algorithm we have used one of the most up-to-date, "Gray Wolf Algorithm" which has been introduced in 2014.

The above model has been implemented on the 136 samples that were collected from the Tehran Stock Exchange between 2015 and 2018. All of the performance evaluation criteria including the classification, accuracy, type error, second-order error and area under the ROC curve showed better performance than the genetic algorithm which was presented and its significance was confirmed by t-test.

Considering the gray wolf algorithm’s high accuracy and its performance compared to the genetic algorithm, it is necessary to use the gray wolf algorithm to predict the bankruptcy of Iranian manufacturing companies either for investment purposes and for validation purposes, or for using internal management of the company

The Central Bank has specific regulations, including asset classification guidelines and how to perform loan’s loss provisioning to oversee banks and credit institutions. The Central Bank seeks to improve the quality of the loan and reduce the amount of non-performing loans by reducing the revenues of the bank or credit institution through imposing fine on balance of each of the categories of non-performing loans. In this study, the effectiveness of this policy has been investigated using the system dynamics method. The results of the research indicate that the application of the above policy would at least lead to a 17% reduction in the amount of non-performing loans’ accumulation. Research suggests that, if the coverage factor of a pledge during the provision of loan, increases the effectiveness of the policy in the fight against the accumulation of non-performing loans will be eliminated. This phenomenon suggests that the Central Bank needs to focus more on preventive regulatory approaches than on a priori methods in monitoring the credit institutions in providing a loan.

Personal financial management behavior mentions how a person reacts when encounters financial events. Decisions of a person in different conditions influence his present and future life as well as economy of society. This study is about personal financial management, and examining factors affecting personal financial management behaviors among Iranian youth is it's purpose. To do this, relationships among financial attitude, financial knowledge, external locus of control and financial management behaviors was examined. This study is a applied and descriptive research. Data gathering is based on questionnaire. The research model is structural equation modeling and examined by Partial Least Square (PLS) method. The findings suggest that, financial attitude and financial knowledge have statistically significant and positive effects on financial management behaviors. In addition, based on our third hypothesis, the moderated role of financial knowledge on the relationship between financial attitude and financial management behaviors is approved by results. The results do not support for the relationship between external locus of control and financial management behaviors and the indirect effect of external locus of control on the relationship between financial knowledge and financial management behaviors.

The present study investigates the impact of financial self-efficacy on personal finance behavior. To this end, the data collected from the questionnaire have been employed. Once the validity and reliability of the questionnaire are confirmed, the relation between financial self-efficacy and the likelihood of holding a specific financial product is studied using the multivariate Probit model. Products are subsequently categorized into two distinct groups based on the type of relationship. Afterward, the relation between financial self-efficacy and the likelihood of holding the number of products in each category is evaluated using the ordered Probit model. In this study, the cluster random sampling method is employed. The results are indicative of a negative and significant relationship between financial self-efficacy and the likelihood of holding bank facilities as well as a positive and significant relationship between financial self-efficacy and the likelihood of holding time deposits, Qarz Al-Hassaneh savings account, insurance, and corporate bonds and stocks. Furthermore, it is inferred that financial self-efficacy has a positive and significant impact on desirable financial behavior and a negative and significant impact on undesirable financial behavior.

In inefficient markets, returns are not distributed normally and they have serial correlations. It is obvious that the price changes are not independent, so there is a pattern in price changes which help investors to gain unusual benefits. One of the patterns which are concerned with an inefficient market is the lead-lag effect. This research investigates the existence of this effect between small and large size portfolios in Tehran Stock Exchange during the period of 2011-2017. This relationship was examined both in short-run by using cross-correlation approach and vector auto-regressive model and long-run by employing cointegration methodology. Cross-correlation matrices imply that there is a lead-lag effect in short-run. Existence profiles and variance decomposition are used for further validation, the results show that all of the shocks were fully absorbed after three weeks but there is no pattern for big portfolios indicating that they absorbed the shocks more rapidly than small portfolios, and also overreaction is observed only in one out of two small portfolios of the sample.With confirming the existence of lead-lag effect in long-run by Cointegration approach, the ability of ECM models in out of sample forecasting is concerned which is measured by root mean squared error and Wilcoxon's signed-rank test. However, the results indicate that the error correction model has superior forecasting performance relative to models without the error correction terms but the Wilcoxon's signed-rank test does not reject the null hypothesis that the two RMSEs are the equal

In consideration of the importance of foreign exchange rate changes in the bank's operations and their profitability, we have attempted to examine the effect of changes in the exchange rates on the bank's operations and their short-term and long-term profitability. In this respect, we have applied Error Correction Model to measure this effect. The related data have also been extracted from exchange rate lists and balance sheets of one of the Iranian commercial banks during 2005-2010. In order to enter and analyze raw data, E-views software has been used. Test results show that changes in exchange rates have a negative effect on the foreign exchange profitability of banks. While, foreign exchange position has a negative effect on profitability. Also, according to the adjustment coefficient, in presence of shock, the model almost takes about 25 days to reach equilibrium.

The purpose of this study is to discuss and show how a general equilibrium analysis can be used to decompose demand elasticity. We apply this framework into electricity market. To do so, we use a general equilibrium model, assuming Iran as a small and open economy. In this model, we separate the contributions from various mechanisms to the price sensitivity of aggregate electricity demand. The contribution of households and activities demand is separated in first step and then the contribution of activities are decomposed to three components: substitution effects, scale effects and re-allocation effects. Our results shows that 100% increase in electricity price leads to 8.78% decrease in electricity demand, out of which 0.32% is related to households and 8.46% is related to sectors. In activities, substitution effects with 5.9% have more important role with respect to other effects. Scale effects and re-allocation effects decline the electricity demand by 1.06% and 0.12% respectively. Also "wholesale and retail services" and "metal ores and other minerals" have highest response to electricity price changes which contribute 2.27% and 1.84% respectively, in total 5.63% substitution effects.

Efficiency improvement in electricity uses leads to a decrease in its demand and consequently a decline in market price of electricity. It is expected that the induced increase in electricity demand due to this price effect offsets part of the primary reduction in consumption; a phenomena known as "Rebound Effects". Hence, ignorance of these effects in policy making causes overestimation of the benefits of efficiency improvement policies.This paper aims to determine the parameters that influence the magnitude of rebound effects theoretically and to evaluate the consequences of an exogenous and costless efficiency improvement in electricity use in the context of a computable general equilibrium model. This model is calibrated using Micro Consistent Matrix (MCM) constructed based on 2001 Social Accounting Matrix (SAM) of I.R.Iran assuming a small open economy. We found that electricity efficiency improvement will result in rebound effects of 14.2%. This means that 14.2% of primary decrease in demand is offset by rebound effects. According to our results, there are significant differences of rebound effects across electricity consuming sectors. Oil and Gas sector demonstrates highest rebound effects. Sensitivity analysis to test the response of rebounds to the specification of elasticity of substitution between electricity and fossil fuels shows that, economy-wide rebound effects changes from 11.6% to 14.2% due to changes in elasticity of substitution from 0.1 to 0.9.