Financial Economics
Teimur Mohammadi; Mohammad Reza Feghhi Kashani; Mahdi Samei
Abstract
The negative correlation between an asset’s volatility and its return is known as the “leverage effect”. This relation is explained by the effect of the return of a firm’s equity on the degree of leverage in its capital structure. If this relation holds, the increased volatility ...
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The negative correlation between an asset’s volatility and its return is known as the “leverage effect”. This relation is explained by the effect of the return of a firm’s equity on the degree of leverage in its capital structure. If this relation holds, the increased volatility resulting from a fall in stock price should be comparable with the decreased volatility resulting from a price rise with the same magnitude and also, this effect should be persistent. Most of the researches in the “leverage effect” examine the relation between volatility and stock return. To examine the effects of both returns and financial leverage on volatility data from the 22 biggest companies from March 2009 to March 2019 in Tehran Stock Exchange are collected. To find the leverage the value of debt in the capital structure of selected companies is calculated using Geske compound option pricing model. The data show the leverage effect only in negative returns and may have a negligible direct connection to the firm leverage.
Financial Economics
Reza Taleblou; mohammad mehdi bagheri todeshki
Abstract
This paper investigates the impact of sentiment as a critical risk factor in the capital market, leading to behavioral deviations in the pricing of financial assets. We propose an estimation of the asset pricing model based on the Stochastic discount factor (SDF) framework, incorporating both traditional ...
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This paper investigates the impact of sentiment as a critical risk factor in the capital market, leading to behavioral deviations in the pricing of financial assets. We propose an estimation of the asset pricing model based on the Stochastic discount factor (SDF) framework, incorporating both traditional and behavioral approaches. By extending the consumption-based asset pricing model (CCAPM) and introducing sentiment into the utility function through the Euler equations and the generalized method of moments (GMM), we analyze the Tehran Stock Exchange.To quantify sentiment, we utilize the market turnover sentiment index as a reliable indicator. Our study covers the period from 1390 to 1399 and encompasses 18 stock exchange groups, consisting of 63 listed companies on the Tehran Stock Exchange.The results indicate that the behavioral SDF model offers higher consistency and efficiency compared to the traditional model, aligning closely with the dynamics observed in the Tehran Stock Exchange. Moreover, the coefficient of sentiment proves to be statistically significant. In terms of risk, the behavioral model demonstrates higher coefficients than the traditional model. Interestingly, both models suggest that market participants exhibit a high time preference factor and demonstrate patience in their investment behavior.
Financial Economics
Mohammad Feghhi Kashani; Teymor Mohammadi; hadi pirdaye
Abstract
Corporates adjust their information voluntary disclosure according to the volatilities they experience in their cash flows. The purpose of this study is to investigate the effects of news concerning risk, ambiguity level, and investors' ambiguity aversion on the policy adopted by firms as to the voluntary ...
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Corporates adjust their information voluntary disclosure according to the volatilities they experience in their cash flows. The purpose of this study is to investigate the effects of news concerning risk, ambiguity level, and investors' ambiguity aversion on the policy adopted by firms as to the voluntary disclosure (conservative or non-conservative) of soft and hard information in the digital industry subset of Tehran Stock Exchange within the period of 2012-2022. Further, we have used the corporate voluntary disclosure lag to capture the disclosure dynamics along with the control variables including the cost of capital, financial leverage and stock liquidity by dynamic panel models to explain the voluntary disclosure behavior of soft and hard information of the corporates. The results indicate that managers of companies active in the digital industry, depending on the type of information available to them for voluntary disclosure conservatively or non-conservatively, respond differently to the news related to risk, ambiguity and ambiguity aversion of investors. That could be due to the nature of the disclosed information (credibility of information for investors). Likewise, the findings confirm the increasing effects of voluntary disclosure of previous periods on the disclosure of subsequent periods, which somehow confirms the existence of inertia in voluntary disclosure policies in the studied industry.
Financial Economics
Reza Taleblou; Parisa Mohajeri; Abbas Shakeri; teymoor mohammadi; zahra zabihi
Abstract
Achieving the correct insight into the structure of connectedness and the spillover of volatilities between different stock exchange industries plays an important role in risk management and forming an optimal stock portfolio. Also, the analysis of inter-sectoral connectedness helps policy makers in ...
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Achieving the correct insight into the structure of connectedness and the spillover of volatilities between different stock exchange industries plays an important role in risk management and forming an optimal stock portfolio. Also, the analysis of inter-sectoral connectedness helps policy makers in designing policies that stimulate economic growth and implementing preventive measures to curb the propagation of systemic risk. In this regard, this article tries to use the data of 3370 trading days during the period of 1388/07/01 to 1402/06/31, encompassing 20 stock market industries (which constitute more than 80% of the Iranian stock market) and applying the connectedness approach based on the vector autoregression model with time-varying parameters (TVP-VAR), to estimate the systemic risk and volatility connectedness of the stock market network. In addition, we implement the minimum connectedness approach in the optimal stock portfolio and compared its performance with two other conventional approaches. The findings reveal that, first; the systemic risk in Iranian stock market is significant and has reached unprecedented figures of 80% in the last three years. Second, the four major export industries (petrochemicals, metals, mining and refining) experience the strongest pairwise connectedness, and among them, base metals appear as one of the most important transmitters of volatilities to the entire stock network. Thirdly, the stock portfolio based on the minimum connectedness method, compared to the minimum variance and minimum correlation methods, shows a better performance based on the criteria of cumulative return and hedge ratio efficiency.
Financial Economics
Gholamhossein Golarzi; Mahnaz Khorasani
Abstract
The exchange rate, as a fundamental variable, alongside other economic variables, has a significant impact on stock returns. Therefore, this study has investigated the effects of the exchange rate and its fluctuations on the pharmaceutical industry's stock returns through linear and nonlinear models ...
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The exchange rate, as a fundamental variable, alongside other economic variables, has a significant impact on stock returns. Therefore, this study has investigated the effects of the exchange rate and its fluctuations on the pharmaceutical industry's stock returns through linear and nonlinear models during the years 2005 to 2021. In this research, first, the exchange rate fluctuations were modeled using the GARCH model. Then, the symmetrical and asymmetrical effects of the exchange rate and its fluctuations, along with the macroeconomic control variables including the healthcare consumer price index, oil price, and industry-specific control variables including asset return ratio, asset turnover ratio, and debt ratio as well as the COVID-19 dummy variable, were investigated on the return of the pharmaceutical industry stock using both linear ARDL and nonlinear NARDL models. The study shows that in both the short and long term, the impact of the exchange rate on pharmaceutical industry stock returns is greater than the impact of exchange rate fluctuations. Additionally, negative shocks of the exchange rate and its fluctuations have a negative relationship with the pharmaceutical industry's stock returns, while positive shocks of the exchange rate and its fluctuations have a positive effect on the pharmaceutical industry's stock return. The study's findings suggest that the impact of positive and negative shocks of the exchange rate and its fluctuations have asymmetric effects on the return of pharmaceutical industry stock. Results show that control variables and COVID-19 have significant effects on pharmaceutical industry stock returns in linear and nonlinear models.1.IntroductionPharmaceuticals, as a strategically vital industry, can significantly contribute to a country’s economic growth and the enhancement of public health. However, a major challenge faced by this industry in Iran is its heavy dependence on imported raw materials and essential machinery, with nearly 60% of the required raw materials being sourced through imports. The pharmaceutical sector in Iran is particularly vulnerable to exchange rate fluctuations, given its high dependency on foreign currency. Consequently, the exchange rate and its fluctuations emerge as determining factors influencing the profitability and stock returns of companies operating in this sector. Divergent perspectives exist regarding how exchange rate fluctuations impact stock returns, with some studies asserting a positive correlation, others a negative one, and some maintaining a neutral stance. Since there is no consensus on the precise nature of the relationship between exchange rate fluctuations and stock returns, especially within the pharmaceutical sector, the present research tried to investigate and compare the effects of exchange rate and its fluctuations on stock returns in the pharmaceutical industry.2.Materials and MethodsUsing linear and nonlinear autoregressive distributed lag models (i.e., ARDL and NARDL), the study examined both the symmetrical and asymmetrical effects of exchange rate fluctuations on the return of pharmaceutical industry stocks during 2005 to 2021. The research also considered macroeconomic control variables, including healthcare consumer price index, oil price, COVID–19 dummy variable, and the variables specific to the pharmaceutical industry (e.g., asset return ratios, turnover ratios, and debt ratios). First, the generalized autoregressive conditional heteroskedasticity (GARCH) model was employed to model exchange rate fluctuations. The long-term linear equation for the return of pharmaceutical industry stocks can be defined as follows:(1) Also, the long-term nonlinear equation is defined as follows:(2) 3.Results and DiscussionThe research findings reveal that, in the short-term period and based on the linear ARDL model, the exchange rate significantly affects the return of the pharmaceutical industry stocks, with exchange rate fluctuations also causing a significant negative impact on stock returns. Moreover, the analysis of the long-term coefficient estimates from the linear ARDL model suggests a positive correlation between the exchange rate and pharmaceutical industry stock returns. Consequently, the results imply that an increase in the exchange rate can boost the competitive power and stock returns of pharmaceutical companies. However, in the long run, exchange rate fluctuations can have a detrimental effect due to heightened uncertainty in the stock market, dissuading investors from engaging in this industry. Additionally, the study indicates that an increase in oil prices results in a decrease in pharmaceutical industry returns, as investors seek profits in alternative markets. Inflation, too, negatively affects pharmaceutical industry stock returns, as heightened inflation fosters uncertainty, reducing investor inclination toward pharmaceutical stocks. Furthermore, the research findings highlight that various factors such as pharmaceutical industry asset returns, asset turnover, debt levels, and the dummy variable of COVID–19 positively impact pharmaceutical industry returns.The results obtained from the nonlinear NARDL model showed that both short-term and long-term negative shocks in the exchange rate and its fluctuations significantly decrease the stock returns of the pharmaceutical industry. In contrast, positive shocks in the exchange rate and its fluctuations positively affect the stock returns of the pharmaceutical industry. Hence, it can be concluded that the exchange rate and its fluctuations have an asymmetrical effect on pharmaceutical industry stock returns in Iran. Unlike the linear ARDL model, the results of the nonlinear NARDL model indicated that inflation and debt levels do not exert significant impact on pharmaceutical industry stock returns in the long run. Additionally, impact of oil prices on pharmaceutical industry returns is significantly negative in the long run, while pharmaceutical asset returns, asset turnover, and the dummy variable of COVID–19 contribute to an increase in pharmaceutical industry returns in Iran.4.ConclusionConcerning the importance of the pharmaceutical industry and the influence of the exchange rate on the stock returns in the Iranian stock market, the present research used ARDL and NARDL models to examine both the linear and nonlinear effects of exchange rate and its fluctuations on pharmaceutical industry stock returns during 2005–2021 in Iran. The research results indicated that, in both the short and long term, the impact of exchange rate is more significant than the impact of exchange rate fluctuations on the returns of pharmaceutical industry stocks. According to the findings, negative shocks to the exchange rate and its fluctuations can lead to a decrease in the returns of pharmaceutical industry stocks, while positive shocks result in an increase. The results suggest an asymmetrical impact of positive and negative exchange rate shocks and its fluctuations on pharmaceutical industry stock returns. In both linear and nonlinear models, the control variables of the study, along with the COVID–19 as the dummy variable, have significant impact in on pharmaceutical industry stock returns. In sum, the findings indicated a significant relationship between the exchange rate and its fluctuations and pharmaceutical industry returns in Iran. However, the impact of exchange rate and its fluctuations on pharmaceutical industry proves to be heterogeneous. It is thus recommended that investors take note of the differing results of linear and nonlinear models and the asymmetric effects of variables, utilizing modern financial engineering instruments to implement appropriate risk-hedging strategies against exchange rate fluctuations.
Financial Economics
Gholam Reza Keshavarz Haddad; Iman Sharifi
Abstract
The book-to-market ratio is known as an anomaly variable in the financial literature. This variable has a high explanatory power in predicting the returns of companies in different capital markets across world; But understanding why it has the power to explain is still a matter of debate. In this study, ...
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The book-to-market ratio is known as an anomaly variable in the financial literature. This variable has a high explanatory power in predicting the returns of companies in different capital markets across world; But understanding why it has the power to explain is still a matter of debate. In this study, we seek a clear understanding of the explanatory power of the ratio of book-to-market ratio in explaining the annual return of cross-sectional data of stocks on the Tehran Stock Exchange. Book value can be divided into two parts: retained earnings and contributed capital, which have different economic meanings for readers of financial statements. Our hypothesis is that the predictive power of the book-to-market ratio arises from a component of book value that could be a good proxy for underlying earnings yield. Using the method of Fama and Macbeth (1973), we regress the annual return of cross-sectional data of companies listed on the Tehran Stock Exchange for the years 2001-2019 on the ratio of book-to-market ratio and its two components. Neither component of book-to-market ratio could eliminate the predictive power of this ratio; however, the ratio of retained Earnings-to-market ratio could show predictive power along with the book-to-market ratio. We contribute to the literature by providing additional evidence from Tehran's Stock Exchange.1- IntroductionThe book-to-market ratio is known as an anomaly variable in the financial literature. It has appeared as a key explanatory variable with high explanatory power in predicting the returns of firms in capital markets across the world, however, understanding the mechanism through which this financial factor functions and its origin of the explanatory power is still a matter of research debates. Empirical researches on the returns and “book to market value” can be divided into two strands. The first group aims to examine the existence of abnormal returns on the ratio of "book to market value" in the stock markets. This stream of works aim to answer the question of whether the "book to market value" is able to predict companies' returns in capital markets or the returns is caused by other sources including random noise. Rosenberg et al. (1985) show, for instance, that in the US capital market, the strategy of the "book to market value" can yield abnormal returns for investors. In terms of this strategy, at the beginning of each month, the shares with a high "book to market value" are bought and the shares that have a low "book to market value" ratio are sold. A relationship between the ratio and average stock returns for the period 1981-1981 in the capital markets of Switzerland, France, Germany and the United Kingdom has also been observed by Coppole, Rollie and Sharp (1992). The second stream of studies on the "book to market value" seeks to understand the cause of its explanatory power. This issue is an active research area and is still subject of discussions and has been studied from various aspects. One of the most highly cited of them is Fama and French (1993), which attributes high returns in stocks with a higher magnitude of "book to market value", to higher systematic risk. In contrast, Daniel and Titman (1997) introduces the hypothesis of equity characteristics and by providing empirical evidence argues that the returns premia on high book-to-market stocks does not arises because of the co-movements of these stocks with pervasive factors. It is the characteristics of the share rather than the covariance structure of returns that appear to explain the cross-sectional variation in stock returns. So, these are not associated with greater risk tolerance. Ball, Gerakos, Linnaeus, and Nikolaev (2020) examines the "book to market value" through its components (retained earnings and contributed capital) in the US capital market. He argues that the ability of "book to market value" to predict the cross-sectional returns is not because of its intrinsic information contents, but it appears as an appropriate proxy for the actual profitability of the firms, because, the retained earnings component of the book value of equity includes the accumulation and, hence, the averaging of past earnings, instead the contributed capital-to-market has no predictive power. HypothesesWe contribute to the literature by providing additional evidence from Tehran's Stock Exchange. Our study aims to provide further evidence to clarify explanatory power of the ratio in predicting the variations of annual returns in cross-sectional data for stocks in the Tehran Stock Exchange. Our hypothesis is that the predictive power of the book-to-market ratio arises from a component of book value that could be an appropriate proxy for underlying earnings yield. Data and Identification methodologyWe use the annual returns and financial statements of all shares traded from the beginning of 2001 to the end of 2020 in Tehran Stock Exchange. Annual returns are calculated from price data recorded and reported in the “tseclient” software and accounting data are downloaded from “codal.ir” website. In this research, financial companies listed in the TSE have not been included in our working sample due to their special nature. Because, by nature of their activities, they have high financial leverage, which is normal for companies active in the financial field. The characteristics might be interpreted as a financially critical situation, whereas, the it is not so for firm that are active in financial fields. The information extracted from the financial statements is matched with the annual return of 1 month after the end of the financial year. The reason for this identification strategy is to make sure that the published financial information affects the share price. For example, if the company's financial year is at the end of March, we will assume that this information was available to the public at the end of April. Findings Following the statistical method of Fama and Macbeth (1973), we regress the annual return for cross-sectional data of companies listed on the Tehran Stock Exchange over the years 2001-2019 on the ratio of book-to-market ratio and its two components as well. Neither component of book-to-market ratio could eliminate the predictive power of book-to-market; however, the ratio of retained Earnings-to-market ratio could show predictive power along with the book-to-market ratio. Table (1) reports the Fama and Macbeth (1973) regressions in which, outcome of interest is returns and determinants of the regression are the log of "Book to Market Value", log of " Retained Earnings to the Market Value " and log of "Contributed Capital to Market value". We include a few controlling variables that are identified theoretically as determinants of returns.Table(1): Contributed Capital and Retained Earnings in the Fama and Macbeth Regression(1)(2)(3)(4)(5)(6)Variables-0.129**-0.128**-0.116**-0.0901**-0.126**-0.103**Log(Market Value)(-2.680)(-2.762)(-2.492)(-2.474)(-2.257)(-2.228)0.498** 0.210** 0.508** Log( Book-to-Maket)(2.744) (2.471) (2.342) 10.53***8.557** 9.914**Log(Retained Earnings to market Value) (2.992)(2.426) (2.890) 0.371***0.004060.255***Log(Contributed Capital) (3.446)(0.0438)(3.343) 0.619***0.560*** 0.415**Binary if profit>0 (3.382)(3.058) (2.256)2.973**-19.64***-15.47**2.429**2.959**-18.34**Constant(2.731)(-2.907)(-2.272)(2.825)(2.534)(-2.806) 3,7943,7943,7943,7943,7943,794#OBS0.1210.1440.1880.0990.1350.189R-Square212121212121# Groups*** p<0.01, ** p<0.05, * p<0.1, t-stats in parenthesisNote: the firms fixed effect regression over 2001- 2021 across 181 firms are reported in the columns. Contributed capital includes all of the book value accounts except retained earnings. Column (1) shows the regression of annual stock returns on the logarithm of "book to market value" in the presence of a control variable, logarithm of market value. The estimated coefficient for "logarithm of book to market value" equals to 0.498 with t-statistic t = 2.74, which is statistically significant at 5 percent critical region. The result is in the same direction with those in previous studies on the "book to market value". In column (2), "logarithm of retained earnings to market value" has been replaced for "logarithm book to market value". The coefficient of " logarithm of retained earnings to market value" is equal to 10.53 and is statistically different from zero at the 1 percent significance level with the estimated t = 2.99. In column (3), two variables "logarithm of book to market value" and "logarithm of retained earnings to market value" are included in the model. The coefficients of "logarithm of retained earnings on market value" and the "logarithm of book to market value" are significant at the conventional significance level. It suggesting that, "logarithm of book to market value" and "logarithm of retained earnings market value" are not able to fully represent the information contained in their competitors, as determinants of the firms' annual returns.The columns (4) and (5), report similar regressions by substituting "logarithm of contributed capital to market value" in place of "logarithm of retained earnings to market value". Once, we include this determinant alone, it significantly impacts (coefficient 0.371 with t = 3.446) annual returns, but if we add "logarithm of book to market value", to the specification "logarithm of contributed capital on market value" loses its significance and its t statistic drops to 0.0438. Meanwhile, the "logarithm of book to market value" remains significant at the 5 percent level. In the column (6), in addition to the "Book to market Ratio "we keep both "logarithm of retained earnings to market value" and "logarithm of contributed capital to market value" in the specification. The coefficient of "logarithm of retained earnings to market value" remains almost with no tangible change 9.914 with and significant, and the coefficient of "logarithm of contributed capital on market value" is appears significant as well.The inability of "logarithm of retained earnings to market value" to absorb the effect of "logarithm of book to market value" can be due to the weakness of this financial account in representing the companies' profitability information. This might originates in the fact that the retained earnings account is not an appropriate representative of the company's profitability. More specifically, this account is the balance of profits that have not been distributed among investors, it is not representative of all the company's acquired profits, and in each period that: (1) the company distributes profits among investors or (2) transfers an amount from this account to another account in equity, a part of the information in the accumulated profit will also be removed from this account. Consequently, this account cannot contain all the profitability information of the company. When the company distributes profits to shareholders, the company's profitability information is removed away from both the retained earnings balance and the book value. For this reason, we simply return the amounts transferred from the retained earnings account to other equity accounts to the retained earnings account and define the adjusted retained earnings account and the adjusted contributed capital as follows:Adjusted retained earnings = retained earnings + legal reserve + plan and development reserve + other reserves + total capital increase from retained earnings until the end of the reported year + total other transfers from retained earnings until the end of the reported yearAdjusted Contributed Capital = Equity - Adjusted Retained EarningsAdjusted retained earnings is the balance of all profits earned by the company during its life and not withdrawn from the company. The adjusted contributed capital is equal to the book value minus the adjusted retained earnings. To test our hypothesis, we separated "book to market value" into two parts (1) "adjusted retained earnings on market value" and (2) "adjusted contributed capital on market value". The significance level of the coefficient of "book to market value" decreases when it is included in the model beside to "adjusted retained earnings to market value", in contrast to the specification that includes the "retained earnings to market value", however, the coefficient of "book to market value" is still significant at the 5 percent significance level. The significance of the coefficient of "adjusted retained earnings to market value" also improves, in comparison to all similar regressions in which unadjusted "retained earnings to market value" are used as determinant. All in all, this evidence shows that a part of the information in "book value to market value" is caused by a variable that is related to the company's profitability, but not all the information in "book to market value" is caused by the company's profitability.
Financial Economics
Nazanin Ghasemdokht; Hamideh Razavi
Abstract
Overdue claims resulting from the lending process can pose a significant credit risk to financial institutions. To mitigate this risk, institutions often acquire guarantees. However, borrowers may encounter challenges when providing adequate and valid guarantees, particularly guarantees with lower risk. ...
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Overdue claims resulting from the lending process can pose a significant credit risk to financial institutions. To mitigate this risk, institutions often acquire guarantees. However, borrowers may encounter challenges when providing adequate and valid guarantees, particularly guarantees with lower risk. The present research focused on loan credit risk, borrower utility, and liquidity risk of guarantees within a private fund. First, data mining and classification methods were applied to a dataset of loans. The random forest algorithm, with a prediction accuracy of 0.986, was found to be optimal for constructing a guarantees composition model. The guarantees composition involves using multiple types of guarantees to secure a loan. Two models were established to generate guarantee compositions with a maximum default rate of 10%. In testing scenarios, the average risk of total default for acceptable combinations stands at 3.94%, a significant improvement compared to the fund loans’ overall default rate of 6.3%. Furthermore, the proposed model increases borrower utility from 4.22 to 4.6, not only reducing the default rate but also enhancing borrower utility.IntroductionWhen providing loans to customers, banks require guarantees due to insufficient knowledge of customers and the default risk. Obtaining guarantees from borrowers is recognized as a solution to reduce default risk in banks, but its impact on risk reduction depends on various factors. The combination and type of guarantees are among these factors, which have received less attention in the literature.The current state of overdue bank claims in Iran is unfavorable, and if conditions persist, it will lead to significant monetary and financial crises with negative effects on various sectors of the economy. In recent years, the ratio of non-performing loans to total disbursed facilities in Iran has been consistently higher, averaging around 5% to 10% higher than the global average. Reduction of the default risk in loans can decrease the ratio of non-performing loans to total disbursed facilities.The present study first intended to create various combinations of guarantees for each loan, followed by predicting the probability of default for each combination. In line with their priorities, borrowers can then select their desired guarantee composition from a list of acceptable combinations.MethodologyTo address the research problem, the study identified common classification methods in data mining by relying on published articles in the field of credit risk. Then, a sample dataset of loans from a financial institution was examined, and the data mining process based on classification methods was applied to the dataset. The random forest method, with a prediction accuracy of 0.986, was ultimately chosen as the approach for constructing the guarantee composition model. Using the previous guarantee compositions, the study developed two models by relying on machine learning techniques. These compositions take into account the perspectives of both the financial institution and the borrower.Final ResultThe two models generate guarantee compositions with a maximum acceptable default rate of 10%. Considering their own priorities circumstances, borrowers can select their desired guarantee composition from the available combinations, which contributes to a reduction in the default rate in the financial institution.
Financial Economics
Saman Hatamerad; Bahram Adrangi; Hossein Asgharpur; Jafar Haghighat
Abstract
The present research aimed to investigate the relationship between Iran’s stock price index and nine macroeconomic variables during 1996–2019. Three methods were employed to reduce uncertainty, namely three Bayesian averaging methods (BMA, BMS, BAS), weighted average least squares (WALS), ...
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The present research aimed to investigate the relationship between Iran’s stock price index and nine macroeconomic variables during 1996–2019. Three methods were employed to reduce uncertainty, namely three Bayesian averaging methods (BMA, BMS, BAS), weighted average least squares (WALS), and Vselect. The experimental results of the Bayesian methods and WALS showed that the exchange rate and the consumer price index are the most important variables among the nine macroeconomic variables considered in the model. Moreover, the results revealed that the exchange rate has a minor impact on the stock price index, while the stock price index exerts a substantial effect on the exchange rate. The findings of Vselect validated the conclusion that these two variables are the primary drivers of stock price estimation and are present in nearly all predictive modelsIntroductionThe harmonization of financial markets with the macroeconomic sector is crucial for stabilizing the economy and achieving the adopted policies. In recent years, several significant studies have been conducted on financial markets, particularly the stock market, highlighting their pivotal role in allocating capital resources efficiently in advanced economies. Empirical evidence supports the view that financial markets have evolved in tandem with all sectors of the economy. Therefore, it can be argued that financial markets constitute one of the most vital components of any country’s economy. Throughout history, major economic crises have resulted from the collapse of financial markets, which underscores their critical significance. The financial market comprises several components, with the stock market being a crucial part. Economists view it as a barometer of a country’s economic health due to its ability to reflect macroeconomic asset prices more accurately than other markets. The uncertainty surrounding stock prices in stock markets is a significant aspect of the entire economy, capable of generating and disrupting unsustainable growth. For investors, the risk of participating in an investment is a crucial consideration. To comprehend total risk, it is beneficial to examine two aspects: systematic and non-systematic risk.The present study aimed to examine the impact of economic factors on stock market prices in Iran with the high degree of risk involved. There is a consensus among economists that asset prices are responsive to economic news, and that stock prices and economic factors are strongly interconnected. Thus, this research investigated the potential impact of macroeconomic factors on the Iranian stock price index from 1996 to 2019 using Bayesian averaging methods, followed by an analysis of the effect size of each variable through the weighted average least square method (WALS).Materials and MethodsResearchers often draw conclusions based on the assumptions of their selected model, assuming that it can accurately predict real-world situations. However, this approach may overlook true uncertainty, leading to non-conservative conclusions. Statistical models comprise two parts: variables and assumptions, and the model selected based on these assumptions to estimate the variables. Uncertainty exists at both levels. For instance, a researcher estimating the impact of influential factors on an independent variable may choose a model based on their assumptions and report their estimates. But is this the best answer? Another researcher with different assumptions may opt for a different model with lower variance and error. In other words, numerous models may fit the sample data equally well but with different coefficient estimates and standard errors. Bayesian model averaging (BMA) is a robust method that aims to remove uncertainty. It assesses the robustness of results to alternative specifications by computing posterior distributions for coefficients and models. This study employed three models of BMA, BMS, and BAS, using various averaging methods to verify the reliability of the results. Moreover, two non-Bayesian methods, namely WALS and Vselect, were used to select the best variables for predicting the optimal models.ConclusionThis study tried to investigate the relationship between Iran’s stock market index and nine macroeconomic variables during 1996–2019 by using the models that identify and limit uncertainty. The models selected include three Bayesian averaging models as well as WALS and Vselect which were used to verify the results obtained. The results indicated that only two variables, the exchange rate and consumer price index, are statistically significant when assuming a uniform distribution of the prior distribution function, which is the assumption of the BMS method. The remaining variables are not statistically significant. Furthermore, the estimates derived from the BMA and BAS models were quite similar, with the exception of less important variables. However, the similarity decreased in the BAS method. Moreover, WALS and Vselect confirmed the results obtained from all the three methods.
Financial Economics
Hossein Talakesh Naeini; Reza Taleblou; Teymor Mohammadi; Parisa Mohajeri
Abstract
Extensive applications of asset pricing in the fields of finance and economics lead to an increasing importance of this issue, which has attracted more attentions of researchers in theoretical and empirical aspects. Due to this issue, the main purpose of this paper is to compare two asset pricing methods ...
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Extensive applications of asset pricing in the fields of finance and economics lead to an increasing importance of this issue, which has attracted more attentions of researchers in theoretical and empirical aspects. Due to this issue, the main purpose of this paper is to compare two asset pricing methods i.e. “Beta” and “stochastic discount factor” in Iran Stock Exchange market. Using the monthly data of Tehran Stock Exchange index return and return of shares of the companies listed in the stock exchange market of Iran during 1379(1) to 1398(6), we have formed 5*5 baskets-called 25 portfolios of Fama and French- to evaluate the efficiency and stability of one factor model (capital asset pricing model) and multi-factors model (Fama and French’s 3 factors model) using Generalized Method of Moments (GMM) estimation method. The results show that the aforementioned methods are not completely superior to each other. In fact, for CAPM model, stochastic discount factor method is more efficient and less stable than Beta method and vice versa for Fama and French’s 3 factors model.
Financial Economics
Mostafa Abdollahzadeh; Hashem Zare
Abstract
The main purpose of this paper is to calculate the entropy of money in the space of Gross domestic product with the approach of econophysics and investigating the effect of stock market development on it. In this regard, by using annual data in the period of 1370-1398 in the framework of Smooth Transition ...
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The main purpose of this paper is to calculate the entropy of money in the space of Gross domestic product with the approach of econophysics and investigating the effect of stock market development on it. In this regard, by using annual data in the period of 1370-1398 in the framework of Smooth Transition Autoregressive Model (STAR), the asymmetric behavior of monetary irregularities around a threshold at different levels of stock market value as a variable of analysis is investigated. The results show that at low levels of current value of the stock market (the first regime), net capital inventory and budget deficit of governments have positive effects and the number of companies admitted to the stock exchange organization have a negative effect on monetary entropy. At high levels of current value of the stock market (Second Regime), net capital inventory has negative effect and government budget deficit continued to have a positive effect on monetary entropy. Based on the results of this study, it is clear that the dynamics of the stock market will reduce monetary entropy, which is itself an indicator of wasting and lacking of access to the resources.
Financial Economics
Vahid Taghinezhadomran; Zahra Mila Elmi; Fatemeh Zahra Husseinpor
Abstract
Banks have a considerable ability to use financial leverage compared to non-bank firms to earn high profits and returns with support of the central bank as a last resort lender. The ability of banks to use leverage depends on internal characteristics such as size, profitability and risk, as well as environmental ...
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Banks have a considerable ability to use financial leverage compared to non-bank firms to earn high profits and returns with support of the central bank as a last resort lender. The ability of banks to use leverage depends on internal characteristics such as size, profitability and risk, as well as environmental variables such as inflation, which affect the Business cycle. This study aims to find the effects of these variables on the dependency of banks on financial leverage in recession and booms periods. To this end, Hodrick-Prescott filter was used to extract business cycles. The Generalized Method of Moments (GMM) based on the data from 18 Iranian banks during 2005-2018 was used in order to test the research hypotheses. The results show that larger banks are more inclined to leverage and economic conditions have no significant effect on this desire. Banks with better financial stability and less risk rely on lower financial leverage in times of economic prosperity. The effect of profitability criteria on the leverage of banks depends on economic conditions. In times of economic prosperity, banks with better profitability have a higher incentive to leverage. Also, how the inflation affects the financial leverage of banks depends on the economic conditions. During an economic boom, inflation encourages more reliance on leverage in banks.
Financial Economics
Hamid Reza Arbab; Hamid Amadeh; Amin Amini
Abstract
This study investigated the factors that leads to economic uncertainty which may influence the petrochemical companies returns in various market conditions regarding their various levels of capital. To meet this object, we used quarterly data on government’s current expenditures, general government ...
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This study investigated the factors that leads to economic uncertainty which may influence the petrochemical companies returns in various market conditions regarding their various levels of capital. To meet this object, we used quarterly data on government’s current expenditures, general government revenues, liquidity, GDP, and exchange rate, as the political variables for the years 1384-1397. Considering the type of available time series, we exercised the ARIMA-GARCH model to create an indicator to show the uncertainty of economic policies. We used the result to estimate the quantile regression model, along with other factors affecting corporate returns, including the price of the OPEC oil basket and the real rate of returns and market exchange rate. The results of this study indicated that in the bearish market, the greatest negative effect of each economic policy uncertainty is on the companies with lesser capital. Moreover, the intensity of this effect decreases as the market tends to change from bearish to bullish, and finally the economic policy uncertainty will have the least impact on companies with bigger capital.
Financial Economics
Firooz Shaghaghi; Asgar Pakmaram; Younus Badavarnahandi
Abstract
Financial development is one of the main goals of economic policymakers to achieve sustainable economic growth. One of the important approaches to financial development is the expansion and deepening of the stock market. However, such expansion needs improvement of good governance, or institutional quality. ...
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Financial development is one of the main goals of economic policymakers to achieve sustainable economic growth. One of the important approaches to financial development is the expansion and deepening of the stock market. However, such expansion needs improvement of good governance, or institutional quality. Given the importance of this issue, the present study investigates the effect of institutional quality indicators (voice and accountability, political stability without violence, government effectiveness, regulatory quality, rule of law, and corruption control) on stock market variables by two Islamic countries group (10 countries) and non-Islamic countries (37 countries) during period from 2002 to 2016 using data panel method. The results showed that the institutional quality components plays an important role in the growth of stock prices relative to the growth of the average return on the whole economy as well as the increase in the volume of stock trading relative to the growth of the total turnover in both groups of countries. However, the impact of these components on the group of Islamic countries is far greater than that of non-Islamic countries. In addition, foreign direct investment, increasing real GDP at a constant price, and government final expenditure at constant price in both groups of countries have a significant impact on the growth of stock trading volumes and stock prices.
