فصلنامه پژوهش های اقتصادی ایران
پیاپی 95 (تابستان 1402)

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.

A review of empirical studies in the field of insurance of upstream oil and gas projects suggests that domestic insurance companies and insurance consortiums in Iran rely on the experience of reinsurance companies to determine premiums and risk conditions. Given the economic sanctions and restrictions on determining precise premiums and conditions for direct insurance and reinsurance of oil assets, it is necessary to establish a method to determine premiums even under normal circumstances that can be referred to international reinsurers. To address this need, the present study adopted an empirical method that uses risk-based valuation and the monetary value at risk (VaR), which covers a wide range of relevant oil and energy insurance aspects. The results showed that the research method can determine the premium of oil assets following international standards, taking into account expert opinions and other domestic considerations.IntroductionOil and gas continue to be among the primary sources of energy globally, hence a crucial and fundamental part of the world economy. The various sectors within this vast field offer significant capacities and resources at a large scale, including research, equipment, and resources for exploration, development, and utilization, as well as the derivatives in the real sector processes. In addition, transportation, exchanges, transactions, physical markets, and stock exchanges also play a significant role in this industry. The world economy is thus highly dependent, both directly and indirectly, on the oil and gas industry. It is evident that the global oil and gas industry involves a vast amount of capital and risk, which is more complex and extensive than one may imagine. The industry interacts with numerous sectors of the economy, with extensive links that exceed beyond a single field. Considering the relatively small share of oil and energy insurance in the portfolio of the insurance industry, there are various factors that prevent the entry of insurance services into this area on a significant scale. One significant obstacle is the lack of scientific studies on determining the value at risk for oil assets, which is a crucial factor in determining the volume and size of insurance premiums for insurable oil and gas assets. Using the valuation and estimation of monetary VaR of oil assets, the present study aimed to develop a method for insurance companies to determine the insurance premium of oil and gas assets. An example was provided to demonstrate the applicability of the proposed method in practice. Materials and MethodsTo conduct a risk-based valuation, the study employed Smith’s method as well as the method proposed by Knapp and Heij in order to estimate the value at risk for oil assets.The NPV for the valuation model of exploration and development risk will be as follows (Smith, 2004): (1)Where CF0 is the drilling cost,  is the probability of a failed well and {CF1, CF2... CFr} are the expected cash flows in case of success of the well and (i) is the discount rate.According to Knapp and Heij (2017), identifying risk factors, which are variables that pose a risk for an oil asset, is crucial in estimating the value at risk. The risk factors are evaluated based on their probability of occurrence.The monetary VaR is estimated based on the insurable value of a physical asset, which is typically the replacement cost or the actual cash value of the asset covered by standard insurance policies.To estimate the value at risk, two probabilities are combined, which are determined based on the total insurable value (TIV). For instance, if there are five risk groups denoted as j (j=1,...,5), vj represents the sum of the insurable value for each type. TIV is defined as the total of all five groups, as follows:            Furthermore, Pinc represents the probability of an event occurring within a year. Pj is the conditional probability of damage in group j occurring in relation to a particular event. Then monetary VaR is then defined as follows: (2)It is important to note that TIV is derived from the method proposed by Smith (2004), which can be used to assess the value of the entire property or its individual parts and components.The insurance rate and premium can be estimated on the basis of the model of exploration and development risk, along with the future discount rate. This estimation includes the initial cost (drilling or installation cost) CF0 and is given by the following equation:NPV, in Relation (1), is replaced by the future value (FV) or the total insurable value (TIV) of the oil asset.CFt = CF0: NPV is equivalent to book value or price determined by official experts or official pricing authorities.PDH: Probability of occurrence of major risks, which is considered equivalent to catastrophic risks leading to total damage.i: It represents risk insurance premium, which is equivalent to the probability of the occurrence of conventional risks that each oil asset faces according to its specific conditions.Now Relation (1) will be changed to Relation (3):  (3)Now monetary VaR is calculated as follows: (4)In equation (4), V is TIV, which is equivalent to FV calculated based on Relation (2).Results and DiscussionThe study used the information on the insurance policy of HP-2000 drilling rig of North Drilling Company.               Table 1 Information on the HP-2000 drilling rigThe value of the drilling rigReinsurance premium rateMarket premium rateInsurance premium12000.0040.00283.4(billion rials)  (million rials)* Source: issued insurance policyThe future value of the drilling rig for one year is described in the following table:Table 2 The future value of the drilling rigNPV iFV12000.00220.0032400(billion rials)  (billion rials)* Source: research resultsThe monetary VaR based on the future value at risk (FV), which is equivalent TIV, for the insured drilling rig is as follows according to Relation (2):Table 3 The monetary VaR of the drilling rigFV=TIV  MVR24000.00220.84.3(billion rials)  (million rials)* Source: research resultCatastrophe risk (Pinc) and the 5-fold decomposed risks of the oil rig are determined based on expert opinion, which will be the basis of monetary VaR estimation.The premium values ​​and the rate calculated based on the monetary VaR were compared to the premium values ​​in both cases of reinsurance and market (Table 4). Premium rate is obtained by dividing premium, which in the proposed methods is equal to Monetary VaR (MVR), by oil rig price.Table 4 Comparison of the insurance premium rate based on the MVR method with the reinsurance and market rateMVR rateReinsurance rateMarket rate0.00350.0040.0028* Source: research resultsConclusionThe present study highlighted the characteristics and significance of the oil, gas, and petrochemical industry within the global economy, emphasizing the extensive interactions of this industry with various sectors of the economy, particularly in the field of insurance.The study used a distinctively innovative methodology which combines Smith’s (2004) risk valuation of oil assets with Knapp and Heij’s (2012; 2017) monetary VaR approach to determine the insurance premium rate. The proposed research method allows for the determination of an insurance premium rate that is equivalent to international reinsurance rates, based on the factual, environmental, and market conditions. The study offers insurance and oil engineering experts the possibility of calculating an appropriate insurance premium rate for an oil project based on the identified risks using empirical and technical knowledge, as considered in the proposed method.

Risk and uncertainty are key factors in making economic decisions. Since individual attitudes towards risk can greatly influence choices, it is crucial to understand the determinants of such preferences in order to predict and comprehend individuals’ behavior. The present study aimed to investigate the impact of several factors on individuals’ attitudes towards risk, specifically the degree of risk aversion, by examining individuals’ optimism and patience (time preference). The study used a questionnaire to collect data from a sample of 304 individuals in Iran selected through random sampling. The research method was a multivariate regression model. The findings indicated that both optimism and income have a significant negative effect on risk aversion, while age has a significant positive effect. Furthermore, the study found that patience does not have a significant impact on risk aversion.IntroductionRisk and uncertainty are critical factors that heavily influence most economic decisions, including investment, education, employment, and the decision to buy a house or insurance. Such decisions involve an element of risk, so they are highly influenced by individuals’ attitudes towards risk. In developing countries, such as Iran, most individuals typically experience unstable incomes, limited access to insurance, and possess few assets to cushion the impact of severe economic shocks. As a result, individuals in these circumstances are more exposed to risk, and these factors can significantly influence their attitudes towards risk. Understanding the determinants of these preferences is crucial to comprehending and predicting people’s behavior, as different attitudes towards risk lead to different choices. The present study was to examine how certain factors, such as optimism and patience (time preference rate), influence individuals’ attitudes towards risk. In addition, socio-economic variables were included as control variables to account for their potential impact.Materials and MethodsTo gather data on individuals’ degrees of risk aversion, optimism, and patience, this study used a questionnaire based on internationally recognized surveys. The model was then estimated by the general multivariate regression through the ordinary least squares (OLS) method.Results and DiscussionThe descriptive information related to demographic variables is presented in Table 1.  Table 1:  Frequency distribution of demographic sVariableVariable levelFrequencyRelative FrequencyGenderMale15549Female16051Total315100Marital statusSingle21970Married9630Total315100AgeLess than 20 years165Between 20-30 years17255Between 30-40 years10032Above 40 years278Total315100Level of educationHigh school41Diploma–BA9931BA–MA13944MA–PhD7323Total315100Employment statusUnemployed5116Retired10Housewife279School student72University student13142Employed9831Total315100The economic situationIncome below 1 million Tomans11236Income between 1–3 million Tomans10637Income between 3–6 million Tomans6320Income above 6 million Tomans3411Total315100Source: research findingsModel  EstimationThe OLS method was used to estimate the model.Table  2.  Model estimation resultsRA                                                                The dependent variableprobabilityt-statCoefficientsVariables0.00-3.71-0.027 * * *Optimism0.0023.070.0003 * * *Wealth0.000-63.60.29 * * *Income0.418-0.81-0.017Patience0.024-26.2-0.19 * *Gender0.0222.310.23 * *Single0.0005.630.044 * * *Age0.2151.240.06Education0.00075.52.16 * * *_Cons29.13F(8,295)304Number of obs0.000Prob > F0.44R-squared0.63Root MSE0.42Adj R-squared     * The coefficient is significant at  10 % level, * * The coefficient is significant at  5 % level and *** The coefficient is significant at  1 % level.Source: research calculationsThe Brush-Pagan and VIF test show that there is no heteroskedasticity and collinearity at estimated residuals.As shown in the table, as the individual’s level of optimism increases, their degree of risk aversion decreases, which is consistent with previous research conducted by Felten and Gibson (2014) and Duhman et al. (2018). In addition, the study found that wealth has a direct and significant impact on risk aversion in Iran, which aligns with the findings of Agassi et al. (2015) and Qanbili (2016). However, this result contradicts the research conducted by Ronald and Grable (2010), and therefore, the effect of wealth on risk aversion warrants further discussion and reflection.Previous research suggests that there is a likelihood that the effect of wealth on risk aversion in Iran may be opposite to that observed in other countries. This could potentially be attributed to errors in measuring wealth In Iran, where information regarding individuals’ assets and wealth is often unclear. In this respect, the present study relied on indicators such as car and house ownership and their estimated values, which were self-reported by the participants and might be subject to bias.The study findings indicated that income has a significant and negative impact on risk aversion in Iran, which is aligned with previous research conducted by Wright (2012; 2014) and Shah et al. (2020). Moreover, it was found that gender has a significant effect on risk aversion, with females being more risk-averse than males. This finding is consistent with Banir and Newbert (2016), Hosseinnejad and Haddadi (2016), and Mohammadi-Majed (2018).The findings also revealed that age has a significantly positive impact on risk aversion in Iran, which is in line with the results of Dankers and Van Suest (1999) and Menadia et al. (2016). Finally, the results showed that time preference rate and education do not have a significant impact on risk aversion in Iran.ConclusionThis research examined the impact of several factors on individuals’ risk aversion in Iran. The investigation of the research hypotheses demonstrated that variables such as optimism and income have a significantly inverse relationship with risk aversion, with higher levels leading to decreased risk aversion. Wealth and age have a significantly positive impact on risk aversion, with higher levels leading to increased risk aversion. Furthermore, the variables of time preference rate and education were found to have no significant effect on risk aversion in Iran. The study also found that married individuals are more risk-averse than single ones, and females are more risk-averse than males.The results indicated that young people, males, and the individuals with higher incomes and lower wealth tend to accept risk more readily. The findings can provide fresh insight for investment consulting and insurance companies in Iran.

In today’s world, countries have come to the realization that their available resources, including human capital, natural resources, and capital, are limited. To use these resources optimally requires that consumption be adjusted and productivity be increased. In this context, the labor force, as a fundamental factor in production, deserves special attention. Several factors such as geographical location, wages, welfare and health indicators, proximity, and population density can impact labor productivity. The present research aimed to investigate the impact of urbanization and its spatial spillovers on the productivity of provincial labor forces during 2006–2019, using the components of the human development index, urbanization rate, population density, and industrial wages. The study revealed the existence of spatial autocorrelation among the investigated provinces. The variables of human development index, urbanization rate, and industrial wage have direct and indirect positive and significant effects on provincial labor productivity, while the population density index has a direct positive effect and an indirect negative effect on labor productivity.IntroductionSustainable urbanization has been a fundamental component of the development of every country. Urbanization can have a significant positive impact on economic activities by providing better services, creating job opportunities, and increasing access to basic services. Cities have the ability to transform low-productivity agriculture into a high-productivity manufacturing industry and cost-effective service sectors. Cities in developing countries are the driving force behind economic growth, accounting for 70% of the gross national product (World Bank, 2009). With the increasing share of the population living in cities, improving the productivity of urban areas has become a priority for many governments and economic consulting organizations (OECD, 2016). Accordingly, cities possess the necessary ability and capacity to influence key economic factors. In this respect, the present study aimed to investigate the impact of urbanization on labor productivity, as a crucial factor for development, by evaluating the economic growth and examining several components of cities. The objective of research was to examine the spatial spillover effects of urbanization on labor productivity in Iran’s provinces, specifically focusing on the savings of density. The study tried to answer the following questions:Is it possible for an urban area to enhance labor productivity at the provincial level?Is there a relationship between labor productivity in a province and the direct and indirect effects of the provincial human development index?Are the external benefits of population density and urbanization (such as benefits from population increase and industrial concentration) responsible for this relationship?Is labor productivity affected by the direct and indirect effects (spillover) of industrial wages?Can the positive side effects of a more efficient urban economy in urban centers be affected by structural problems caused by rapid and dense population growth?Materials and MethodsThe basic model used in this study is as follows: The panel spatial econometric method was employed to analyze the spatial spillovers and geographic space involved in the impact of urbanization on provincial labor productivity. The Stata software was used to examine the final data, and a square matrix was created through GeoDa software in order to estimate the model with the spatial econometric method. This matrix represents the proximity between the provinces and assigns a value of 1 to neighboring provinces and 0 to non-neighboring provinces. Stata software packages were then used to standardize the provincial neighborhood matrix, and a vector was obtained by multiplying the matrix by the vector of each variable. The obtained vector was entered as an explanatory variable in the model, and its coefficient expresses the spatial effect. Based on the evaluated processes, the final model is as follows:+ ConclusionFirst, the estimated coefficients of the human development index and industrial wage of the labor force indicate that an increase in these factors within each province has a positive effect on labor productivity. Furthermore, the positive effects of these factors spill over into neighboring provinces. In this respect, competitive markets play a role in improving labor attraction factors within the province, thereby preventing the departure of skilled labor. With the implementation of necessary policies, job skills are promoted, and the permanent departure of highly skilled labor force is reduced.Second, the estimated coefficients of the urbanization variable show that the increase in urban population and demand, in addition to the training of specialized labor in cities, leads to the recruitment of skilled labor. This in turn has a positive spillover effect, increasing the urbanization rate of neighboring provinces. As a result, it leads to an increase in labor productivity in the neighboring provinces.Finally, the direct effect of population density in a province has a positive impact on labor productivity. However, the indirect effect of population density on labor productivity is complex. While creating a positive external effect due to economies of scale, the indirect effect is also countered by the crowding effect caused by population density. The crowding effect is actually due to the lack of sufficient infrastructure in line with population growth in the province, which leads to negative spillovers of neighboring regions into the province.The various effects observed provide strong evidence for a positive relationship between urbanization and labor productivity. These effects suggest that, under the appropriate conditions, cities have the potential to generate significant employment opportunities and stimulate growth and development not only within the city and province but also across the country. Cities can create sustainable jobs and increase productivity, thereby maximizing the ability to innovate, respond to market demand, and benefit from the advantages of dense markets.

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.

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.

Migration is a global phenomenon that is driven by a variety of reasons, which can be categorized as push and pull factors. Push factors refer to negative circumstances that compel individuals to leave their country of origin and seek a better life elsewhere. In contrast, pull factors are positive conditions that attract individuals to a particular destination. These may include better job opportunities, greater security, better healthcare, and improved educational opportunities. It is important to note that the push and pull factors that influence migration can vary depending on an individual’s characteristics.In recent decades, one of the most significant developments in migration in developing countries has been the increasing participation of women in migration flows, including their growing independent migration to developed countries. Women represent a significant portion of human capital in these communities, so their involuntary migration can have negative impacts on the development process. It is thus crucial to identify and understand the underlying factors of women’s migration, which can inform appropriate policies to address the issue. The present study used experimental data from 28 developing countries and the generalized method of moments (GMM) to examine the interactive effect of globalization and entrepreneurship on women’s international migration during 2011–2020. The results indicated that improving women’s entrepreneurial conditions has a significantly negative impact on international migration, while increasing the level of education and poverty index can have a significantly positive impact. However, the social, political, and economic aspects of globalization moderate the negative effect of entrepreneurship on women’s international migration. In other words, with the reduction of barriers and geographical boundaries, women are more willing to engage in entrepreneurship and gain new job experiences in a different country. Moreover, improving the index of gender equality and individual freedoms in the country can have a significantly negative effect on the process of international migration of women. Policymakers can reduce migration by improving gender equality and individual freedoms, revising laws and regulations related to women’s business space, and supporting entrepreneurship.IntroductionIt is crucial to understand the gender complexities surrounding women’s international migration to maximize the benefits of migration for women— who constitute half of the migrant population—and to minimize its socio-economic costs for them, their families, and their countries of origin. This understanding can also help prevent negative consequences in immigration destinations. Women often migrate internationally to escape social restrictions or to improve their families’ living conditions and provide a better prospect for their children. However, excessive migration, especially among young women with high education and skills who are in their reproductive age, can have dangerous consequences, such as exacerbating the demographic crisis, destabilizing the family foundation, and reducing economic growth at the national level.The history of independent international migration of women, separate from men and families, only dates back to the last few decades. Therefore, a comprehensive understanding of the reasons behind this phenomenon requires consideration of the new and emerging variables affecting human society and women’s lives. One such variable is globalization, which eliminates geographical borders and allows for the free flow of ideas, goods, services, and capital.In addition to eliminating geographical borders, globalization has facilitated the movement of people and labor between different countries, which has also affected women’s international migration. In addition to eliminating geographical borders, globalization has facilitated the movement of people and labor between different countries, which has also affected women’s international migration. Furthermore, the growth of women’s economic participation and entrepreneurship has increased their material independence, which has influenced their international migration. Finally, increasing the degree of social, economic, and political globalization of countries by providing the ground for women’s entrepreneurship can also affect their international migration.The structural approach emphasizes that women’s migration is influenced by a variety of factors, each with varying degrees of effectiveness. Moreover, the economic, social, and political structures of the host society play a significant role in women’s decision-making regarding international migration. Women’s income and financial independence are crucial factors in their decision to migrate, which is directly influenced by women’s entrepreneurship. In fact, entrepreneurial power enables women to take advantage of opportunities in different parts of the world. Entrepreneurship is the basic driver of social health and wealth and a powerful engine of economic growth that promotes the necessity of innovation. Entrepreneurship is not only necessary to take advantage of new opportunities, improve productivity, and create employment but also to address some of the biggest challenges of society (Women’s Entrepreneurship Report, 2021). Innovative women entrepreneurs bring new solutions to the market with new sources of value that are not provided by competitors. International entrepreneurs outside their national borders also contribute to the global competitiveness of their country’s economy.Materials and MethodsThe study used multivariate regression analysis, a panel data approach, the generalized method of moments (GMM), and Stata software to estimate the interactive effect of globalization and entrepreneurship on women’s international migration. The statistical population of the study consisted of 28 developing countries used as the study sample. The model included the women’s international migration index as the dependent variable, while social, political, and economic globalization, women’s education, economic misery index, gender equality, and individual freedoms were considered as explanatory variables and effective factors of women’s migration.Results and DiscussionThe research model utilized in this study is a panel data type, which provides a more efficient estimation by limiting the problem of heterogeneity of variance, reducing collinearity between variables, and increasing the degree of freedom compared to cross-sectional data and time series (Baltaji, 2005). In addition, the present research model can be considered as dynamic according to De Brau (2019) and Sultana and Fatima (2017), where the dependent variable intercept appears as an explanatory variable on the right side of the equation. The mathematical expression of the model is as follows: The dependent variable of the model is International Migration of Women (MWit), and the explanatory variables include social (SGit), political (PGit), and economic (EGit) globalization, Women’s Entrepreneurship (WENTit), Women’s Education (WEDUit), Economic Misery index (EMit), Gender Equality (GEit), and Personal Freedoms (PFit).This research used a dynamic panel data model in which the dependent variable appears as an explanatory variable with an interval on the right side, a correlation is created between the disturbance component and the mentioned variable, and the estimation results are skewed. Therefore, the GMM was used to estimate the variables. This method does not require detailed information on the distribution of disturbance sentences, based on the assumption that the disturbance sentences in equations with a set of instrumental variables are not correlated. Two tests were conducted to ensure the suitability of GMM for model estimation. The Sargan test was used to test the validity of instrumental variables. A Sargan statistical probability value greater than 5% indicates the non-correlation of the instruments with the disturbance components, and hence, the instruments used in the estimation are valid. Second, the first-order AR(1) and second-order AR(2) residual correlation tests were employed. The results indicated that there is first-order serial correlation in all cases of estimation of disturbance sentences, but there is not second-order serial correlation or clear distortion.  Table 1. Estimation results of the research modelSecond StateFirst StateDependent variable: International migration of woment StatisticCoefficientt StatisticCoefficientExplanatory Variables▼6/0060/1876/0430/192LnIMW (-1)------3/4610/158LnSG------2/4120/035LnPG------3/9560/163LnEG-------4/208-0/179LnWENT3/7180/102------LnSG*WENT2/2560/061------LnPG*WENT3/4800/147------LnEG*WENT3/1140/2243/1650/231LnWEDU2/0170/0612/0260/058LnEM-5/512-0/346-5/387-0/351LnGE-4/968-0/186-4/914-0/190LnPF0/6126/1750/6086/03Sargan test statistic0/0000/0530/0000/057AR(1)0/7030/310/6910/30AR(2)228228Number of obs88Number of group2828Obs per groupConclusionAs economic, social, and political globalization increased in selected countries, so did the migration of women. The dissolution of geographical borders, the inability of developing economies to compete with developed counterparts, the disappearance of subcultures, and the familiarity of women with the culture and language of the destination countries all contributed to the increase in women’s international migration. Moreover, extroversion in foreign policy and the conclusion of understandings and bilateral/multilateral agreements of regional and international organizations for regular, easy, quick, and low-cost legal migration procedures also play a role in this context. The increasing trend of migration of skilled and expert women from developing countries to developed countries often results in improved employment opportunities, greater material benefits, and higher social status for these women.The establishment of entrepreneurship as a viable career path for women, along with equal business opportunities as men, and the ability to implement women’s creative plans and ideas in developing countries, could lead to their strong presence as valuable members of society. This, in turn, would strengthen women’s self-confidence and motivation to migrate, while also reducing the push factors for emigration.Gender equality in the home country can increase women’s hope of achieving a better life and reduce their desire to migrate abroad. In addition, individual freedoms in the home country can strengthen women’s desire to stay and work towards achieving greater freedom and a more liberal culture that aligns with their desires and aspirations. Improving the educational system, such as a one percent increase in enrollment in the third middle school, is an important factor in promoting social mobility for women since it provides opportunities for the development of individual talents, higher income, better social status, and improved living conditions, which can encourage women to migrate. Other factors leading to an increase in women’s migration include the decline in economic performance, economic difficulties, and a rise in the misery index in the home country, along with the expectation of a better situation in destination countries.The interactive effect of globalization and entrepreneurial environment on women’s international migration in the selected countries was found to be significantly positive. However, the lack of positive and constructive effects of social, political, and economic globalization on women’s entrepreneurship has moderated the reducing effect of entrepreneurship on international migration. Globalization has actually made it more likely for women entrepreneurs to seek business opportunities abroad, thus increasing their migration.