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
Iman Dadashi; Vahid Omidi
Abstract
Considering the impact of global variables on stock market industries, the present study relied on the Quantile-on-Quantile Connectedness (QQC) and Structural Vector Autoregression (SVAR) to examine the impact of geopolitical risk fluctuations on the volatility of the petroleum products, chemical products, ...
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Considering the impact of global variables on stock market industries, the present study relied on the Quantile-on-Quantile Connectedness (QQC) and Structural Vector Autoregression (SVAR) to examine the impact of geopolitical risk fluctuations on the volatility of the petroleum products, chemical products, metal ores, and basic metals sectors in the Tehran Stock Exchange. The analysis focused on the period from January 1, 2020, to December 24, 2024. The results from the QQC model revealed that fluctuations in geopolitical risk exhibited the strongest correlation with the volatility of the petroleum products industry index at extreme deciles, indicating a significant impact. In other industries, the highest susceptibility to geopolitical risk fluctuations had occurred when their volatility was in the 9th and 10th deciles. In addition, the SVAR model results indicated that the immediate response of industry index volatility to geopolitical risk shocks was positive across all cases. Over 360 periods, this response converged to a positive value, reflecting the persistence of the shock. The cumulative response analysis further demonstrated an exponential increase in all industries, suggesting a rising trend in the effect of geopolitical risk over time. Specifically, after 360 periods, the volatility of the petroleum products industry index increased by 0.34, chemical products by 0.06, metal ores by 0.03, and basic metals by 0.06.IntroductionRecently, the Tehran Stock Exchange (TSE) has been grappling with various risk factors, including the government budget, uncertainties in domestic and foreign policies, the Al-Aqsa Storm and Promise Fulfilled operations, interest rates, the exchange rate, and inflation. Notably, the TSE has not consistently mirrored the behavior of global markets across different periods. For instance, at the height of the COVID–19 pandemic, when most global stock markets experienced significant downturns, the TSE reached historic record highs. Conversely, at times when global markets were on the rise and commodity prices increased, the TSE entered a decline. This divergence was primarily due to internal risks unique to the TSE, which prevented the domestic market from benefiting from global market growth. The present study aimed to examine the impact of geopolitical risk fluctuations on the price index volatility of selected industries listed in the TSE. The industries were selected based on their specific characteristics and their sensitivity to geopolitical risks.Materials and MethodsThe study employed the Quantile-on-Quantile Connectedness (QQC) model to examine the relationship between the overall stock index and Islamic Treasury Bonds (Sukuk). To this end, the QVAR(P) model, which enables the estimation of relationships across different quantiles, is utilized as follows: (1)In this equation, and represent the vector of endogenous variables with a dimension of . The vector τ denotes the quantiles within the range [0,1], while P indicates the lag order of the QVAR model. Additionally, μ(τ) is the vector of conditional means, is the coefficient matrix, and is the vector of error terms. Subsequently, the Generalized Forecast Error Variance Decomposition (GFEVD) for an F-step-ahead forecasting, which represents the impact of a shock in series j on series i, is expressed as follows: (2)In this equation, denotes the variance-covariance matrix of the error terms. The vector is the standard basis vector or unit vector of dimension , with its the i-th element equal to one and all other elements set to zero.In this case, the rows of do not sum to one. Therefore, is standardized to obtain the scaled GFEVD: (3)Using this, the overall adjusted connectedness index (quantile-to-quantile) is calculated as follows: (4)In Equation (4), the higher the Total Connectedness Index (TCI), the higher the market risk.The analysis also used the Structural Vector Autoregression (SVAR) model. In the QQC model, the volatility of geopolitical risk was analyzed in relation to each of the other variables in the model, with results extracted accordingly. The SVAR model followed the same principle. Consequently, four models were estimated.The VAR model in this study is represented in its general form as follows: (5)Where is a vector containing the volatility of geopolitical risk and the index of each industry analyzed individually. The matrices to contain the coefficients of the lagged variables, and represents the residuals, which follow a normal distribution with zero mean and covariance . However, the shocks derived from Model (5) are not structural. To address this, the following model is used, allowing constraints to be imposed on matrices A and B: (6)In Equation (10), represents the structural error terms. The relationship between the VAR and SVAR models is expressed as .Results and DiscussionThe results indicated that geopolitical risk had a significant and varying impact on different industries within the TSE. This impact is influenced not only by each industry’s volatility level but also by the distribution of risk quantiles and industry indices. The QQC results revealed that the petroleum products industry was the most sensitive to geopolitical risk, particularly in extreme quantiles, where its connection to geopolitical risk reaches its peak. This finding suggests that during periods of high volatility, risk transmission accelerates. Similarly, in the chemical, metal ore, and basic metals industries, increased volatility heightened their susceptibility to geopolitical risk shocks. Notably, when these industries experience higher volatility quantiles, their connection to geopolitical risk strengthens across all levels. Structural shock analysis using the SVAR model indicated that all industries exhibited a positive immediate response to geopolitical risk volatility shocks. This reaction is strongest in the short term and gradually weakens over time. Among the industries analyzed, the petroleum products sector displayed the highest sensitivity, with an increase of 1 unit, while the impact on the chemical products, metal ore, and basic metals industries was 0.6, 0.3, and 0.5 units, respectively.ConclusionAccording to the findings, the relationship between geopolitical risk and the petroleum products industry is strongest in extreme quantiles. For other industries, the QQC model identifies two key patterns: first, when geopolitical risk volatility is in the 9th and 10th quantiles, it has the greatest impact on these industries; second, when the industries’ own volatility is in the 9th and 10th quantiles, they show the highest susceptibility to geopolitical risk across all quantiles. In addition, the results from the SVAR model indicated that the impact of geopolitical risk shocks on these industries would remain positive even after 360 periods. In other words, geopolitical risk shocks have a lasting effect on the volatility of the industries analyzed in this study.
Financial Economics
Ali Nassiri Aghdam; Mahtab Moradzadeh
Abstract
The leverage ratio reflects a company’s relative reliance on capital and debt. Higher leverage ratios, indicating greater dependence on debt relative to equity, ceteris paribus, increase the firm’s financial risks. The present study examined the effect of income tax and deductible financial ...
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The leverage ratio reflects a company’s relative reliance on capital and debt. Higher leverage ratios, indicating greater dependence on debt relative to equity, ceteris paribus, increase the firm’s financial risks. The present study examined the effect of income tax and deductible financial costs on the firm’s leverage ratios. The data was collected from companies listed on the Tehran Stock Exchange during the period 2011–2020. Theoretically, higher effective tax rates and deductible financial costs are expected to lead to higher leverage ratios. These hypotheses were tested using a dynamic panel data model and the generalized least squares (GLS) method. The findings revealed that, after accounting for control variables, the effective income tax rate has no significant impact on the leverage ratio. However, financial costs have a positive and significant relationship with the leverage ratio.IntroductionModern theories of capital structure are rooted in Modigliani and Miller’s (1958) theory of irrelevance of capital structure. According to their theory, under conditions of perfect competition, with no taxes, symmetric information, and the absence of bankruptcy and agency costs, a firm’s value is independent of its financing sources. However, in practice, a firm’s value does depend on its financing sources. In another study, Modigliani and Miller (1963) relaxed the assumption of no taxes and demonstrated that introducing corporate income tax affects the firm’s capital structures. Firms tend to use more debt than equity to optimize financing costs because interest expenses are deductible from taxable income. However, increasing debt also raises the risk of insolvency. This trade-off in leveraging a firm’s capital structure suggests that firms determine an optimal debt-to-equity ratio by balancing the tax savings and bankruptcy costs associated with higher debt levels (Fama & French, 2005). In other words, a higher corporate income tax rate increases the tax shield, incentivizing firms to use more debt. The motivation to take the advantage of tax shield, in turn, increases the risk of bankruptcy (Faccio & Xu, 2015). In Iran, corporate income is subject to a flat tax rate of 25%. Additionally, financial costs, including interest expenses, are treated as deductible, reducing the firm’s tax burdens. Moreover, dividends are tax-exempt due to the absence of personal income tax. These conditions create an incentive for companies to distribute profits and rely more heavily on debt to finance their operations. The present study aimed to test this hypothesis by relying on empirical data.Materials and MethodsThis study used the following regression model to examine the effect of income tax and financial costs on corporate capital structure. The dependent variable is the leverage ratio (lev), calculated as total debt divided by total assets (Chakrabarti & Gruzin, 2019; Rajan & Zingales, 1995). The independent variables are the effective tax rate (ETR) and financial costs (FC). The effective tax rate is determined by dividing tax payments by pre-tax income (Graham, 1996), while financial costs are calculated by dividing interest expenses by total debt (Hossain, 2015). To control for the effects of other factors, the research included several control variables: tangible assets (Gas, 2018; Rajan & Zingales, 1995), growth opportunities (Titman & Wessels, 1988), profitability (Li, 2020), company size (Gas, 2018; Panda & Nanda, 2020), and non-debt tax shields (Chakrabarti & Gruzin, 2019; Gas, 2018; Karadeniz et al., 2009). Before estimating the model using panel data, it was necessary to determine the appropriate data type by using the Limer’s F test. The Hausman test was conducted to decide whether fixed effects (FE) or random effects (RE) is the more suitable estimation method (Gujarati, 2022). Based on the Hausman test results, the fixed effects method was deemed the most appropriate for estimating the model. Moreover, the Wooldridge test and the Wald test were used to evaluate autocorrelation and heteroscedasticity, respectively. Since the p-values in both tests are below 0.05, the null hypothesis was rejected, indicating the presence of autocorrelation and heteroscedasticity in the model. Finally, the generalized least squares (GLS) method was applied to ensure the efficiency of the results (Baltagi, 2008).Results and DiscussionThe GLS method was used to estimate the panel model. As shown in Table 1, the probability of the Wald statistic is less than 0.05, indicating the statistical significance of the regressions (Torres Reyna, 2007). The results of the model estimation revealed that the coefficient for financial costs is significant, and its sign is as expected. Specifically, as financial costs increase, the capital structure becomes more leveraged. This finding aligns with the predictions of static trade-off theory and is consistent with the results of studies such as Akhtar and Massoud (2013). Table 1. Results of Model Estimation Based on Alternative Specifications (Dependent Variable: Leverage Ratio)(5)(4)(3)(2)(1)Variables1.08*(0.57)0.24***(0.095)0.17*(0.095)0.24***(0.095)0.20**(0.096)Financial Cost0.64*(0.34)0.04(0.041)0.05(0.041)0.04(0.04) 0.06(0.040)Effective tax rate -0.17***(0.028)-0.14*** (0.028)-0.18***(0.028)-0.14***(0.028)Tangible assets0.91(0.16)-0.03**(0.015) -0.03**(0.015)-0.02(0.015)Profitability 0.08***(0.02) -0.02***(0.004) -0.03***(0.004)Growth opportunities -0.06***(0.008)-0.05***(0.008)-0.06***(0.008) Size3.52(5.69)0.19(1.81)1.15(1.84) Non-debt tax shield0.59***(0.10) Leverage(-1)164.270.00120.530.00140.500.00121.650.00103.980.00Wald chi2P- value 1.007***(0.057)0.9***(0.057)1.00***(0.056)0.58***(0.012)InterceptP=0.00Arellano-bond test for AR(1) in first differencesP=0.132Arellano-bond test for AR(2) in first differencesP=0.07Hansen test15401540154015401540Observation*P<0.1, **P<0.05,***P<0.01 Standard errors in parenthesesTable 1 presents the model estimation results using the GLS method in Column 1. Columns 2, 3, and 4 display the results of robustness check, with additional control variables included. Column 5 shows the outcomes of the model estimation using the Difference GMM method. A key aspect to note is the consistency of the estimation results. AR1 and AR2, as well as the Hansen test, are related to the GMM estimation in the fifth column.Source: Research findingsThe coefficient for the effective tax rate is not significant, meaning that changes in the effective tax rate do not influence the firm’s decisions regarding their capital structure. Although this finding is unexpected, it is consistent with the results of studies such as Pinto and Silva (2021), Saeedi and Mahmoudi (2011), and Alipour et al. (2015). These researches found the effect of the effective tax rate on the financial leverage ratio to be insignificant. Therefore, the hypothesis that the effective tax rate has a positive effect on financial leverage cannot be confirmed.ConclusionThe estimation results indicated that financial costs have a positive and significant effect on financial leverage. This finding supports the idea that the deductibility of financial costs encourages economic agents to rely more heavily on debt. The results also suggested that the introduction of personal income tax and the taxation of dividends incentivizes companies to reconsider their profit distribution policies and rely more on internal financing rather than debt. This conclusion is consistent with the findings of Haji et al. (2022).
Financial Economics
Reza Taleblou; Mohammad Mehdi Bagheri Todeshki
Abstract
AbstractThe current study examined the influence of sentiment as a key risk factor in capital markets, which contributes to behavioral deviations in the pricing of financial assets. The stochastic discount factor (SDF) framework was used to propose an estimation of the asset pricing model, incorporating ...
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AbstractThe current study examined the influence of sentiment as a key risk factor in capital markets, which contributes to behavioral deviations in the pricing of financial assets. The stochastic discount factor (SDF) framework was used to propose an estimation of the asset pricing model, incorporating both traditional and behavioral approaches. An attempt was made to extend the consumption-based capital asset pricing model (CCAPM) and incorporate sentiment into the utility function through Euler equations and the generalized method of moments (GMM). To measure sentiment, the analysis utilized the market turnover sentiment index as a reliable indicator. The data covered 18 stock exchange groups, including 63 companies listed on the Tehran Stock Exchange, over the years 2011 to 2020. The results showed that the behavioral SDF model demonstrates higher consistency and efficiency than the traditional model, aligning more closely with observed market dynamics in the Tehran Stock Exchange. Furthermore, the sentiment coefficient was found to be statistically significant. In terms of risk, the behavioral model demonstrated higher coefficients than the traditional model. Notably, both models indicated that market participants exhibit a high time preference factor and display patience in their investment behavior.IntroductionOne of the most important approaches to asset pricing is the asset pricing model based on the stochastic discount factor (SDF), from which most asset pricing models can be derived within a general framework combining macroeconomics, finance, and mathematics. The model is based on the concept of a random discount factor (Cochrane, 2000; Foldes, 2000). Shefrin (2008) derived the behavioral SDF model by introducing market sentiment as a random discount factor. A key premise of the behavioral SDF model is that, despite investor sentiment, mistakes are made. As long as sentiment remains in the market, stock prices will not reflect their true value; they may be either inflated or deflated. This has a significant impact on asset pricing and leads to fluctuations in the SDF, as investors exhibit mass behavior, such as excessive optimism or pessimism toward the market (Shefrin, 2008). The primary objective of this study was to address the following questions: Is the experimental SDF in the Tehran Stock Exchange traditional or behavioral? Does sentiment influence asset pricing? Which model better explains investor behavior, asset market fluctuations, market bubbles, and inflated or deflated stock values?Materials and MethodsIn general, the pricing patterns of capital assets can be analyzed using two approaches: Behavioral and traditional. In recent years, economists have introduced new traditional asset pricing models based on the SDF framework. However, these models often fail to align with real-world outcomes, primarily due to their reliance on rational assumptions and the lack of consideration for behavioral factors. Using Shefrin’s Log-SDF theorem and incorporating sentiment into the utility function, the present study estimated the empirical SDF through moment equations and the GMM with both traditional and behavioral approaches.Shefrin’s approach involves estimating the log-SDF as the sum of fundamental components and emotions (Λ). The fundamental variables included in the SDF are total consumption growth (g), the market’s relative risk aversion coefficient and the market’s time discount factor ). The formal equation relating to the Log-SDF and market behavior is as follows: In the framework of the traditional classical model, market sentiment is assumed to be zero and Ln(m) is equal to (Shefrin, 2008). In the present article, both the traditional and Shefrin’s behavioral models are estimated and compared. Shefrin presents the following figure, comparing the behavioral SDF with the traditional SDF:Figure 1. Comparison of Behavioral SDF and Traditional SDF Source: Shefrin (2008)The analysis relied on the seasonal data from 2011 to 2020. The macroeconomic data included private sector consumption, money supply, exchange rates in the free market, the volume of demand deposits in banks and credit institutions, and the gold price per ounce. Initially, the capital market data sample consisted of 18 stock groups, totaling 130 shares. However, due to missing data for some stocks during the timeframe, the final sample was reduced to 63 stocks, as shown in the table below. An attempt was made to include a diverse selection of stocks, representing both high and low volatility, as well as winner and loser stocks from each group. The stock prices were obtained from the TSE website based on the daily closing prices, and the average quarterly returns were calculated for all the stocks in the sample. For the sentiment data across the 18 stock groups, the market turnover sentiment index was calculated for each group. Finally, the average sentiment index of stock market turnover and the average sentiment index of stock price fluctuations across the groups in the sample were estimated.Results and DiscussionThe results of the behavioral SDF model with the assumption of the market turnover sentiment index by different stock market groups are as follows: Table 1. The Results of the Estimation of the Behavioral SDF Model of the Stock Groups in the Tehran Stock ExchangeStock groupThe results of the estimationThe statistic JThe probability of test statistic J βP-ValueMetallic minerals0.99 0.039 0.0204.020.67Sugar0.99 0.034 0.0183.60.73Ceramic Tile0.99 0.055 0.0116.040.41Rubber and plastic0.99 0.083 0.0055.230.51Financial intermediation0.99 0.047 0.0314.040.54Investment0.99 0.031 0.0135.520.35Metals0.99 0.030 0.0195.750.33Bank0.99 0.091 0.0495.640.22Transportation0.99 0.059 0.0211.50.90Car0.99 0.063 0.0305.390.36Metal products0.99 0.079 0.0114.350.49Equipment and machinery0.99 0.031 0.0035.060.53Non-metallic minerals0.99 0.021 0.0036.490.37Electrical devices0.99 0.055 0.0205.480.48Oil products0.99 0.061 0.0068.010.23Paper0.99 0.031 0.0014.630.59Cement0.99 0.035 0.0065.690.45Chemical products0.99 0.061 0.0046.990.32 Source: Shefrin (2008)The general tools used in the behavioral SDF test for all groups are as follows: GSKS (-1), Gexch (-1), Gm (-1), Ghesab (-1), Ggold (-1)The results and estimations indicated several key points. First, the time preference factor (β) is close to one in three cases, indicating that people in the society are patient and have a strong desire to save. Second, the risk tolerance coefficient (γ) in both behavioral SDF models is higher than in the traditional SDF model. This difference arises from the inclusion of the emotion variable in the behavioral model, which aligns with the theoretical expectations that investors do not always behave rationally and are often risk-seeking. Third, the ϵ coefficient, which represents the share of sentiment in the utility function (indicating optimism and pessimism), varies between 0.04 and 0.001 for different stock market groups in the sample when the turnover sentiment index is considered. Fourth, the results showed that the risk-reward ratio of the behavioral SDF model is higher than that of the traditional SDF model in the TSE. Fifth, when considering the average quarterly returns of the stock sample as a proxy for risky assets, the risk premium compared to short-term and one-year deposits is 0.039. The behavioral SDF model, assuming the turnover sentiment index, yields a risk premium of 0.035, suggesting that the behavioral SDF model’s risk-reward results are close to real-world observations. Sixth, the Hansen-Jonathan (HJ) distance criterion, used to compare the performance of non-linear models based on the GMM method, indicated that the SDF model performs better and more efficiently. Moreover, the coefficients of the model were calculated and placed into the LOG-SDF equation, and the two models were compared graphically. ConclusionAccording to the results, the behavioral SDF model aligns more closely with the realities of the TSE than the traditional model, with the sentiment coefficient being statistically significant. The risk tolerance factor in the behavioral model is higher than the traditional model, and in both models, individuals exhibit a high time preference and a degree of patience. When the charts of the behavioral and traditional SDF models intersect, it signifies a market where sentiment is zero, and stock prices are efficient. However, when the behavioral SDF exceeds the traditional SDF, it suggests that investors are overly optimistic, pushing prices above their true value. This scenario can lead to market overvaluation, potentially resulting in sales queues or a shift to negative sentiment after a large volume of transactions. For instance, in 2019 and early 2020, the market was inefficient, with prices falling below their intrinsic value. During this period, the behavioral SDF was lower than the traditional SDF, reflecting pessimism in the market. The two charts converged, signaling that prices were undervalued. As we approached the end of 2020, the gap between the behavioral and traditional SDF charts widened, indicating that investors became overly optimistic, thus driving prices beyond their intrinsic value. This reinforces the importance of considering emotional factors, which are absent in traditional models, and demonstrates how the behavioral model can help in asset pricing. Similar to Barbaris (2018), the findings of the present study suggest that by incorporating transaction volume as a sentiment indicator, we can better understand market fluctuations and identify market bubbles, in line with deviations from the inherent value of shares. Furthermore, like the results of Lu et al. (2000), this research showed that the experimental SDF in the TSE is behavioral and volatile.
Financial Economics
Masood Baghbani; GholamReza Keshavarz Haddad; Hossein Abdoh Tabrizi
Abstract
AbstractThis study examined the relationship between dividend policy (measured by dividend yield and dividend payout ratio) and stock price volatility in the Tehran Stock Exchange. Using fixed effects and random effects regression models developed by Baskin (1989) and Allen and Rachim (1996), the study ...
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AbstractThis study examined the relationship between dividend policy (measured by dividend yield and dividend payout ratio) and stock price volatility in the Tehran Stock Exchange. Using fixed effects and random effects regression models developed by Baskin (1989) and Allen and Rachim (1996), the study analyzed the data of 200 public firms listed on the TSE that consistently paid dividends from 2010 to 2020. The results indicated a significantly negative relationship between dividend policy and stock price volatility. Additionally, firm size was negatively correlated with stock price volatility, with this relationship proving statistically significant. Consequently, managers can partly control the stock risk and influence investors’ decisions through a firm’s dividend policies. Stock price volatility in emerging markets, particularly the Tehran Stock Exchange, is substantially influenced by macroeconomic fluctuations, so considering these factors notably affects the coefficient sizes.IntroductionDividend policy has long been a central focus in financial research, especially concerning its impact on stock price volatility. As a form of return to shareholders, dividend payments play a significant role in shaping investment decisions. In this respect, the present study aimed to investigate the effect of dividend policy—measured through the dividend payout ratio and dividend yield—on stock price volatility in the Tehran Stock Exchange (TSE), using the data of 200 firms over the period from 2010 to 2019. The research aimed to address the following questions: How does the dividend payout ratio affect stock price volatility in the TSE? What is the impact of dividend yield on stock price volatility in the TSE? How do seasoned offerings and macroeconomic factors influence the relationship between dividend policy (dividend payout ratio and dividend yield) and stock price volatility?Materials and MethodsThe research used a sample of 200 firms listed on the TSE from 2010 to 2020 in order to examine the effect of dividend policy on stock price volatility. Using panel regression analysis, the study evaluated the effects of the dividend payout ratio and dividend yield, with control variables such as firm size, earnings volatility, debt ratio, and growth. The data was sourced from Codal (www.codal.ir) and TSETMC (www.tsetmc.com). Stock price volatility was measured through the Parkinson estimator, which calculates volatility based on weekly high and low prices, thereby minimizing distortions from daily price limits on the exchange.Results and DiscussionThe empirical results revealed a statistically inverse relationship between dividend yield and stock price volatility, supporting the duration effect hypothesis. Higher dividend yields contribute to more stable prices, as stocks with larger dividends are less sensitive to changes in the discount rate. This finding is consistent with earlier studies by Baskin (1989), Hussainy (2011), and Mingli et al. (2016). The relationship remains robust across different model specifications. However, no significant relationship was found between the payout ratio and stock price volatility when both dividend yield and payout ratio were included, which is likely due to multicollinearity. When dividend yield was excluded, the payout ratio became significant at the 10% level, showing an inverse relationship with volatility (Table 1). Control variables such as firm size and growth significantly influenced volatility, with higher growth correlated with higher volatility. Debt levels, initially insignificant, became significant when total debt was considered. Adjusting for seasoned equity offerings reduced the effect of dividend yield on volatility but still maintained its significance. Macroeconomic volatility, measured by TEDPIX fluctuations, had the largest impact on stock price volatility, highlighting the sensitivity of TSE to Iran's unstable macroeconomic environment.Table 1. Multicollinearity of Payout Ratio and Dividend Yield, and Its Effect on Regression Results(5)(4)(3)Variable -0.094***(0.032)-0.088***(0.034)DivYield-0.0046*(0.0028) 0.0015-(0.0024)PayoutRatio0.019(0.070)0.023(0.071)0.023(0.070)EarningVol-0.015**(0.0065)-0.015***(0.0063)-0.015***(0.0061)Size0.020***(0.0070)0.021***(0.0069)0.021***(0.0070)Growth-0.035(0.053)-0.026(0.053)-0.026(0.053)DebtRatio0.650***(0.178)0.652***(0.178)0.526***(0.083)ConstantYesYesYesTime Fixed EffectYesYesYesIndustry Fixed Effect169216921801Num of Observation0.4890.5891692R2Source: Research resultsTable 1 shows the results of assessing the effect of dividend policy on stock price volatility, taking into account the multicollinearity between dividend yield and payout ratio. In Specification (3), where all explanatory variables are included, a significant negative relationship is found between dividend yield and stock price volatility, while the payout ratio remains insignificant. Specification (4), which excludes the payout ratio, shows that the dividend yield remains significantly negative. In Specification (5), when dividend yield is excluded, the payout ratio becomes significant at the 10% level, suggesting the presence of multicollinearity between the two variables. The dependent variable is stock price volatility, measured using Parkinson’s method, with the models controlling for firm size, growth, debt ratio, and fixed effects.ConclusionThis research examined the effect of dividend policy on stock price volatility in the TSE, using dividend yield and payout ratio as key indicators. A sample of 200 public firms listed that consistently paid dividends from 2010 to 2020 was selected, and a panel regression analysis was conducted to assess the effect of the indicators. The results revealed a significantly negative relationship between dividend yield and stock price volatility, while the payout ratio was not significant due to multicollinearity. However, when dividend yield was excluded, the payout ratio became significant at the 10% level, also showing a negative relationship with volatility. The findings support the duration, rate of return, and signaling effects, and are consistent with prior studies by Baskin (1989), Hussainy (2011), and Mingley et al. (2016). Among the control variables, firm size and growth were significant, and redefining the debt ratio to include total debt made it significant at the 10% level. The results from alternative specifications using net dividend yield and payout ratio were consistent, offering valuable insights for investors and managers in predicting and managing stock price volatility.
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 leverage effect. This relationship is explained by the effect of a firm’s equity return on the degree of leverage in its capital structure. If this relationship holds, the increased volatility resulting from ...
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The negative correlation between an asset’s volatility and its return is known as leverage effect. This relationship is explained by the effect of a firm’s equity return on the degree of leverage in its capital structure. If this relationship 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 this effect should also be persistent. Most research on the leverage effect has examined the relationship between the behavior of returns and return volatility. The present study aimed to examine the relationship between return volatility, returns, and the debt ratio. The data were collected from 22 biggest companies listed on the Tehran Stock Exchange for the period from March 2009 to March 2019. The value of debt in the capital structure of the selected companies was calculated using the Geske compound option pricing model. According to the results, the existence of an asymmetric effect on returns only during bearish market conditions, alongside the instability of this effect, indicates that the debt ratio cannot explain the behavior of returns and return volatility.1.IntroductionExtensive research on return volatility and its modeling reflects the considerable attention and importance this topic holds within various financial domains. The sheer number of scientific inquiries into volatility modeling and prediction underscores its significance in financial discourse, playing a pivotal role in both theoretical and empirical realms (Kambouroudis et al., 2021). Uncovering the influential factors affecting return volatility and gaining insights into their impact can contribute to a deeper understanding of return volatility. The leverage effect, which denotes the negative relationship between an asset’s return and its return volatility, suggests that as an asset’s return increases, its volatility decreases and vice versa. A common explanation attributes the divergent behavior of stock returns and return volatility to the debt ratio in a company’s capital structure (Aït-Sahalia et al., 2013). When a company’s value increases, assuming the debt value remains stable, the relative return on equity will rise more than the overall company return because the total stock value is less than the total company value. Therefore, equity in a company with a higher debt ratio will exhibit greater volatility compared to the overall company, with this difference depending on the equity ratio in the company’s capital structure. This relationship with the debt ratio also leads to a systemic and inverse change in equity return volatility relative to its own return. When negative stock returns lead to a decrease in equity value relative to the fixed amount of debt, the debt ratio increases, resulting in an anticipated increase in stock volatility in the future. Conversely, positive stock returns are expected to have the opposite effect. The market value of a company’s equity affects the value of its debt. This research aimed to examine the ability of debt ration to explain the observed leverage effect. Therefore, accurately estimating the debt ratio and the value of the debt is crucial. In this line, the present inquiry investigated the relationship between stock return volatility and the debt ratio in the case of companies listed on the Tehran Stock Exchange.2.Materials and MethodsThis study used the model proposed by Figlewski and Wang (2000) in order to investigate the leverage effect. A distinctive aspect of the current research lies in the calculation of the debt value and the debt ratio using the Geske compound options pricing model (Geske, 1979).The sample of the study consisted of 22 non-banking companies selected from the top 30 listed on the Tehran Stock Exchange. Seven banking symbols and one symbol with insufficient information were excluded from the analysis. Banking symbols were excluded due to the unique nature of the banking business, which significantly influences debt performance (Damodaran, 2013). Data on prices, number of shares, and debt structure for these companies were systematically collected from 2009 to 2019. The study relied on quantile regression as the analytical approach. Quantile regression is particularly robust in scenarios where errors deviate from a normal distribution or outliers are present in the data. This method allows for model estimation without being constrained by assumptions typical in ordinary regression, such as homoscedasticity and the influence of outliers on coefficient estimation.3.Results and DiscussionIf the leverage effect, characterized by the negative relationship between return volatility and stock returns, were solely due to returns influencing the debt ratio, one would expect this effect to be consistent across positive and negative returns. Additionally, assuming the effect of returns on the debt ratio remains stable over time, one would anticipate a stable effect on return volatility as well. The findings indicated asymmetric effects of returns on return volatility, with a notable difference between positive and negative returns. Moreover, over time, both the magnitude and significance of this effect diminish. Another objective was to explore the direct effect of the debt ratio on return volatility. Similar to the previous case, the data suggested differing effects of the debt ratio during upward and downward trends. When the debt ratio increases due to declining returns, there is a consistent relationship observed between return volatility and the debt ratio. Conversely, during upward trends, the relationship between the debt ratio and return volatility is inverse. Furthermore, in assessing the stability of the effect of debt ratio on return volatility, the coefficients of lagged debt ratios were not significant, with only the coefficient of the current period’s debt ratio showing meaningful impact over the study duration.4.ConclusionAccording to the results, if a leverage effect exists, it manifests primarily in bearish market conditions (associated with an increasing debt ratio), and this effect is not stable over time. Consequently, the debt ratio alone cannot fully explain the relationship between return behavior and return volatility.
Financial Economics
Soheil Rudari; Ali Mohammad Ahmadi; Vahid Omidi
Abstract
One of the primary concerns of the Iranian National Pension Fund is managing its investment portfolio. In this respect, the present study aimed to examine the long-term investment portfolio, the largest subset of which is V-sandoq. The analysis used the R2 connectedness approach proposed by Naeem et ...
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One of the primary concerns of the Iranian National Pension Fund is managing its investment portfolio. In this respect, the present study aimed to examine the long-term investment portfolio, the largest subset of which is V-sandoq. The analysis used the R2 connectedness approach proposed by Naeem et al. (2023) over the period from September 17, 2013, to September 22, 2023. The study focused on the immediate influence and susceptibility to influence of the stocks within the National Pension Fund. The results showed that, in terms of net influence and susceptibility, the stocks of Group 1 (i.e., Kechad, Foulad, Kegol, and Sheranol) were the most influential, transferring risk to the network. Conversely, the stocks of Group 2 (i.e., Shepas, Pasa, Shekabir, and Vebshahr) were the most influenced by the network. Therefore, risk is transferred from Group 1 stocks to the network, impacting Group 2 stocks the most. In network analysis, during a bear market with a threshold of -4%, there is a high degree of connectivity among the stocks in the portfolio. This suggests that portfolio adjustments are necessary under bear market conditions. Conversely, in a bull market with a threshold of +4%, there is no connectivity between the stocks, indicating that no portfolio adjustments are needed under such conditions.1.IntroductionIn recent years, Iran has consistently faced challenges with pension funds and the inability to generate adequate income to pay retirement salaries. With the number of retirees expected to increase in the coming years (particularly from the 1980s generation), effective management of the investment portfolio of the National Pension Fund’s subsidiaries has become increasingly critical. Many state-owned companies were transferred to the National Pension Fund to finance retired pay from their profitability. However, budget evidence indicates that over 80% of retirement salaries are still financed through the government budget. This underscores the importance and necessity of revising the investment portfolio of the National Pension Fund’s investment holdings. In this respect, the present study aimed to examine the portfolio management of one of the largest subsidiaries of the National Pension Fund, namely the Investment Company of the National Pension Fund or V-sandoq, over the period from September 17, 2013, to September 22, 2023. The study used the vector autoregression (VAR) model with time-varying parameters and R2 connectedness, as an immediate response, proposed by Naeem et al. (2023). The immediate impact analysis of variables on/from each other was chosen because any national, regional, or global event has immediate effects, and providing an appropriate response in portfolio management is of great importance.2.Materials and MethodsThe study employed the TVP-VAR algorithm and the Kalman filter introduced by Antonakakis et al. (2020), in conjunction with the approach proposed by Naeem et al. (2023). The key econometric structure of the TVP-VAR model is outlined below. For the sake of simplicity, it is presented in the form of a first-order VAR. Thus, the TVP-VAR model is as follows:(1) (2) Time-varying parameters and time-varying error variances are essential components for the generalized impulse response functions (GIRF) and generalized forecast error variance decomposition (GFEVD) developed by Koop et al. (1996) and Pesaran and Shin (1998). These components underpin the connectivity approach of Diebold and Yılmaz (2012, 2014). To obtain GIRF and GFEVD, the TVP-VAR needs to be converted to TVP-VMA by applying the Wold representation theorem. According to this theorem, GIRFs i j,t (K) at a forecast horizon K do not assume or depend on the ordering of shocks, providing a more robust interpretation of VAR models compared to standard IRFs, which are sensitive to the order of variables in the econometric system. The GIRF approach reflects the dynamic differences between all variables jjj. Mathematically, it can be expressed as Equation (3):(3) (4) Subsequently, GFEVD ψij,t(K)\psi_{ij,t} (K)ψij,t(K) represents the unique contribution of each variable to the forecast error variance of variable iii, interpreted as the percentage impact of one variable on the forecast error variance of another variable. This can be expressed as Equation (5):(5) The criteria for GIRF and GFEVD can help determine how much variable iii is influenced by others and how much it influences others. Three metrics are used for this purpose.First, we must determine how much other variables in the system influence variable iii. This is obtained by summing the error variance shares for variable iii relative to variable jjj. The influence from others is then calculated using Equation (6):(6) Second, the impact of variable iii on others in the system is calculated through the measurement known as influence on others. This measurement is derived by summing the effects (error variance) that variable iii imposes on the forecast error variance of other variables:(7) The total connectivity index (TCI) is calculated based on the Monte Carlo simulations presented by Chatzanzinou et al. (2021). It demonstrates that the self-variance share consistently exceeds or equals all cross-variance shares. Since the average co-movement of the network is expressed as a percentage, which should be between [0,1], TCI needs to be slightly adjusted:(8) Finally, the TCI definition is modified to obtain pairwise partial connectivity index (PCI) scores between variables iii and jjj as follows:(9) 3.Results and DiscussionFigure 2 illustrates the temporal dynamics of stock influences received from other stocks. It shows the extent to which each stock has transferred or received risk from others. The stocks above the zero line indicate a net influence on the network, while those below indicate a net reception from the network during the examined period. Notably, Kechad, Foulad, Kegol, and Sheranol (Group 1) predominantly acted as influencers, transferring risk to the network. In contrast, Shepas, Pasa, Shekabir, and Vabshahr (Group 2) exhibited the highest reception from the network. Therefore, it can be inferred that external shocks transfer risk from Group 1 to the network, notably impacting the stocks in Group 2.It is crucial to recognize that this influence/reception patterns vary over time and exhibit significant fluctuations. Specifically, the chart shows that the influence/reception of stocks on/from the network decreased with the outbreak of the COVID–19 pandemic from January 19, 2021. Conversely, the disclosure of the letter regarding the increase in petrochemical feed rates on May 7, 2023 heightened the risk transfer from petrochemical stocks to the studied network. This underscores that external shocks do not uniformly affect the portfolio under review, necessitating separate examination of each. Figure1: Net Influence/Reception of Stocks on/from Each Other Source: Research findings4.ConclusionThe results of the long-term portfolio analysis indicated varying levels of interconnectedness influenced by economic, political, military, and health conditions—with the connectivity averaging around 45%. This reflects a high risk for the long-term portfolio. In terms of net influence and reception, Kechad, Foulad, Kegol, and Sheranol (Group 1) generally exerted influence by transferring risk to the network. In contrast, Shepas, Pasa, Shekabir, and Vabshahr (Group 2) predominantly received risk from the network. Thus, during external shocks, risk tends to shift away from Group 1 stocks, thus impacting Group 2 significantly. The outbreak of the COVID–19 pandemic on January 19, 2021 led to a decrease in the influence/reception of stocks on or from the network. Conversely, the disclosure of an increase in petrochemical feed rates on May 7, 2023 heightened risk transfer from petrochemical stocks to the studied network. Concerning the network analysis, there is a high degree of connectivity among the stocks in the portfolio during a bear market with a threshold of -4%. This suggests that portfolio adjustments are necessary under bear market conditions. In bearish markets, it thus becomes imperative to select stocks that have less connectivity. On the contrary, in a bull market with a threshold of +4%, there is no connectivity between the stocks, indicating that no portfolio adjustments are needed under such conditions. Hence, while the examined portfolio is optimal during bull markets, adjustments are essential during bear markets to mitigate risks associated with high connectivity.
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.
