The lack of reliable information disclosure contributes to information asymmetry, which in turn raises the equity cost of capital. Voluntary information disclosure can mitigate unsystematic risk and this reduction can enhance investor confidence through financial signaling. When investors perceive a company as more resilient to specific market fluctuations, the company’s beta—a measure of systematic risk—tends to decrease. This shift in risk perception can ultimately lower the equity cost of capital. Furthermore, when a company improves the quality of its disclosures, it not only affects its own valuation, but can also influence the broader market. In this sense, the disclosure practices of one company can shape public perception and impact the risk assessments of other companies as well. This study explored the mediating role of information asymmetry in the relationship between voluntary information disclosure and the equity cost of capital, utilizing the Capital Asset Pricing Model (CAPM). To achieve the research objectives, a sample of 159 companies listed on the Tehran Stock Exchange (TSE) from 2018 to 2023 was selected. Panel data analysis and multivariate linear regression were employed to test the hypotheses. The findings indicated that voluntary information disclosure did not significantly affect the equity cost of capital. Moreover, the mediating variable of information asymmetry also did not significantly influence the relationship between voluntary information disclosure and the equity cost of capital. This lack of significance might be attributed to the generally low level of voluntary disclosure, inefficiencies within the Iranian capital market, and the limited scope of disclosed information. Additionally, the absence of financial analysts and insufficient investor attention had hindered voluntary disclosures from effectively reducing information asymmetry in the capital market.
Financially distressed firms are actively seeking ways to minimize tax-related cash outflows. During the COVID-19 pandemic, as financial constraints intensified, tax savings emerged as a vital source of internal financing for these firms. As a result, financially distressed firms are more likely to adopt tax avoidance strategies. This study aimed to investigate the impact of financial distress on tax avoidance and how this relationship manifested during the COVID-19 pandemic. The research sample consisted of 162 firms listed on the Tehran Stock Exchange (TSE) from 2017 to 2022. The findings revealed that financial distress had a positive and significant effect on tax avoidance, indicating that firms experiencing greater financial distress were more likely to engage in tax avoidance throughout the study period. Additionally, the COVID-19 pandemic did not significantly moderate the relationship between financial distress and tax avoidance. While this study contributed to the existing literature on the effects of financial distress on tax avoidance, it also enhanced our understanding of how financial crises, particularly those resulting from COVID-19, influenced this relationship. Moreover, contrary to the initial research hypotheses, the findings suggested that COVID-19 did not significantly impact the relationship between financial distress and tax avoidance. Keywords: Financial Distress, Tax Avoidance, COVID-19 Pandemic, Tax Discount. JEL Classification: H26, G32, M41, M48 Introduction In times of financial distress, firms often deplete a significant portion of their cash reserves, exacerbating their financial challenges. This state of distress drives companies to seek ways to reduce tax-related cash outflows, increasing the likelihood that financially distressed firms will engage in tax avoidance (Brondolo, 2009). Several studies (e.g., Edwards et al., 2016; Richardson et al., 2015; Mokhtari, 2019; Hajiha et al., 2017) provide evidence supporting a positive relationship between financial distress and tax avoidance. The COVID-19 pandemic has introduced unprecedented economic challenges and uncertainties for firms worldwide. In response to these uncertainties, managers are compelled to develop various strategies, with tax avoidance emerging as a preferred method for generating internal cash flows. This study aimed to examine the impact of financial distress on tax avoidance and investigate the moderating effect of the COVID-19 pandemic on the relationship between financial distress and tax avoidance. Our research contributed significantly to the existing literature. First, our findings enhanced the body of research on tax avoidance among financially distressed firms (e.g., Edwards et al., 2016; Dang & Tran, 2021; Sadjiarto et al., 2020; Putri & Chariri, 2017; Nugroho et al., 2020; Mokhtari, 2019; Qavi Panjeh & Gharib, 2018; Hajiha et al., 2017). Second, by considering both macroeconomic and firm-level factors that influenced corporate tax strategies, this study deepened our understanding of how firms utilized tax avoidance as a strategy during periods of uncertainty. This study examined the following hypotheses: H1: Financial distress is associated with tax avoidance. H2: The COVID-19 pandemic moderates the relationship between financial distress and tax avoidance. Materials & Methods Data were collected from 162 firms listed on the Tehran Stock Exchange (TSE) between 2015 and 2022, resulting in a total of 972 firm-year observations. The sample excluded firms from the insurance, financial, and banking sectors. Tax avoidance was measured as the difference between a firm's cash taxes paid adjusted for any tax refunds receivable and the product of its pretax book income and the statutory tax rate. This measure was then scaled by the book value of the firm's assets. A firm was considered to engage in tax avoidance when its cash taxes paid were less than its pretax income multiplied by the statutory tax rate. Since part of the tax paid in the current period might pertain to taxes determined in prior periods, the tax expense reported in the income statement was used in place of cash taxes paid for this analysis. Financial distress was assessed using the Altman Z Score. The COVID-19 pandemic served as the moderating variable represented as a dummy variable with a value of 1 for the COVID-19 period and zero otherwise. The years 2019 and 2020 were designated as the COVID-19 outbreak period. To test the research hypotheses, regression models were estimated using the Generalized Least Squares (GLS) estimator. Findings Table 1 presents the results of the GLS regression analysis regarding the impact of financial distress on tax avoidance, as well as the moderating effect of COVID-19 on this relationship. Column 1 shows the effect of financial distress on tax avoidance. The findings indicated that the coefficient for financial distress was positive and statistically significant, suggesting that financial distress was associated with increased tax avoidance. Furthermore, the results revealed that the coefficient for the interaction term (COVID*FD) was not statistically significant, indicating that the COVID-19 variable did not have a significant effect on the relationship between financial distress and tax avoidance. Consequently, the model estimation results did not support our second hypothesis. Discussion & Conclusion Tax avoidance is a strategy used to minimize tax liabilities. Theoretical arguments and empirical evidence suggest that financially distressed firms have a stronger incentive to engage in tax avoidance as tax savings can provide an alternative source of financing. Furthermore, during crises, such as the COVID-19 pandemic, the significance of tax savings increases for firms facing heightened financial challenges. This study investigated the moderating effect of COVID-19 on the relationship between financial distress and tax avoidance. The findings indicated that financial distress positively affected tax avoidance, supporting the notion that financially distressed firms are more likely to adopt tax avoidance strategies. These results are consistent with previous theoretical frameworks and empirical studies, including Edwards et al. (2016), Dang and Tran (2021), Mills and Newberry (2005), Sadjiarto et al. (2020), Putri and Chariri (2017), Nugroho et al. (2020), Richardson et al. (2015), Hasan et al. (2017), Akamah et al. (2021), Dyreng and Markle (2016), Hajiha et al. (2017), and Mokhtari (2019). Moreover, while several studies indicated that firms adopt aggressive tax strategies to mitigate the adverse effects of uncertainty (Lee et al., 2021; Guenther et al., 2019; Huang et al., 2017), this study found that the COVID-19 outbreak did not significantly moderate the relationship between financial distress and tax avoidance. The contributions of this study enrich the growing body of literature on corporate tax strategies during times of crisis. The findings have important implications for both corporate managers and investors. For corporate managers, the results underscore the importance of tax avoidance strategies. When considering tax avoidance, managers should weigh the benefits, such as reduced tax liabilities and increased cash flow, against potential costs, including audit expenses, penalties, and reputational damage that may arise.
Financial instruments in the capital market, such as debt and equity financing tools, along with the establishment of funds, play a pivotal role in advancing financing methods, ensuring timely project execution, and the successful completion of infrastructure projects. Despite their potential, project funds—an innovative financing method in the capital market—have not gained widespread acceptance. This study aims to conduct a diagnostic analysis of the current model of project financing through project funds and propose corrective solutions. Employing a qualitative approach, this research is categorized as applied in terms of objectives and descriptive-survey in terms of data collection. Through document analysis (reviewing books, articles, and notes) and expert interviews, the challenges associated with financing via project funds were identified and categorized into three main themes: process and structural challenges, environmental challenges, and legal and regulatory challenges. Drawing on international experiences, corresponding solutions were proposed, including the facilitation of laws and regulations, revision of the project fund's charter, and reconsideration of corporate governance practices.Keywords: Project Fund, Financing, Financing Funds, Capital Market.JEL Classification: O16, G32, G23 IntroductionProject financing has been extensively studied, yet specialized research on financing through project funds remains limited. Most existing studies focus on the financial capacity of these instruments within the capital market, with only a few exploring their structure and integration with other financing tools. Notably, no study has comprehensively addressed the challenges and corrective measures for project funds in Iran's capital market. Project funds, as financial engineering instruments, aim to facilitate project financing in the capital market. However, since their introduction in 2017, they have seen limited implementation. In contrast, leading countries typically finance projects through private equity investment funds and by offering project company shares in the capital market. This study seeks to identify the challenges of project financing via project funds and propose solutions based on insights from capital market experts and international best practices. Materials & MethodsData were collected through document analysis, including reviews of books, articles, reports, and expert interviews. Purposive sampling was employed, selecting nine experts based on their expertise, educational background, and professional experience. Thematic analysis, following the Attride-Stirling (2001) approach, was conducted at three levels: basic themes, organizing themes, and global themes. This method identified 52 key statements and 11 basic themes, which were categorized into three organizing themes and one global theme. The findings were validated by a focus group comprising a university faculty member, experts, and managers from the Securities and Exchange Organization's Research Center, ensuring credibility and reliability.FindingsThe thematic analysis revealed that the global theme is "challenges in financing through project funds." The process and structural challenges encompass 7 basic themes, the environmental challenges consist of two basic themes, and finally, the legal and regulatory challenges include two basic themes. Discussion & ConclusionThis paper examines project financing through the establishment of project funds, addressing its operational, legal, and regulatory aspects. While project funds hold significant potential for financing projects in the capital market, their adoption has been limited, with only one instance of utilization to date, primarily due to structural deficiencies and fundamental challenges. To address these issues, this study proposes targeted solutions categorized into three areas: (1) process and structural challenges, including centralizing financial resources under the fund manager, setting a defined lifespan for the fund with penalties and incentives, revising the technical supervisory entity's responsibilities, reconsidering mandatory underwriting and market-making requirements, incorporating preferred shares, and separating the boards of the project fund and project company; (2) environmental challenges, such as encouraging active government participation, facilitating fund establishment through regulatory support, and increasing investor participation via specialized investment funds; and (3) legal and regulatory challenges, with the most critical solution being the strengthening of the legal and regulatory framework to ensure clarity and reduce uncertainties in the operation of project funds. These solutions are elaborated using a descriptive-analytical approach to provide a comprehensive roadmap for improving the adoption and effectiveness of project funds.
The performance of the financial and banking sectors in countries is vital for achieving economic growth and development objectives. The size of the informal economy can significantly influence the effectiveness of financial markets and institutions; however, this issue has received relatively limited attention in prior research. This study investigates the impact of the shadow economy's size on the performance of financial markets and banks in BRICS countries from 2000 to 2020. Given the interconnectedness of financial markets and the banking sector, a simultaneous equation system was employed to evaluate the effects of the shadow economy on both sectors concurrently. The Seemingly Unrelated Regression (SUR) technique was utilized for this analysis. The results indicate that the size of the shadow economy negatively affects the performance of both financial markets and banks. Therefore, it can be concluded that the shadow economy serves as a significant determinant of financial market and institutional performance. Additional findings of the study reveal that political stability, per capita income growth, and human development positively influence the performance of financial markets and banks, while inflation and natural resource rents have a detrimental effect. Furthermore, globalization enhances financial market performance but exerts a negative influence on the banking sector. In light of these findings, strategies aimed at reducing the size of the informal economy, promoting transparency, enhancing political stability, improving human development indices, and controlling inflation could considerably strengthen the performance of financial markets and institutions.Keywords: Informal Economy, Financial Market Performance, Banking Sector Performance, SUR Panel.JEL Classification: G32، G21، O17IntroductionThe performance of a country's financial and banking systems is heavily dependent on the overall health of its economic sectors. Efficient financial intermediation requires the mobilization of sufficient and effective resources, which are essential for both the private and public sectors. In the private sector, informal and clandestine activities absorb a significant share of these resources, potentially disrupting the financial intermediation process. In the public sector, an increase in hidden activities results in diminished government tax revenues, thereby depleting available financial resources. Consequently, government-backed policies designed to support financial intermediation encounter challenges, as a portion of the economy's financial resources becomes redirected toward government financing, constraining the funds available for private sector lending. Furthermore, the financial and banking sector can only make a substantial contribution to investment financing when resources are allocated to transparent, formal activities that engage with the economic system’s taxation and statistical processes. The diversion of financial and banking resources to shadow economic activities impairs the allocation of these resources to productive and formal endeavors, potentially undermining the efficiency of financial and banking operations. This study examines the impact of the shadow economy on the performance of the financial market and banking sector, focusing particularly on emerging economies and the evolving economic and trade relationships between Iran and the BRICS nations, which serve as case studies. Materials & MethodsThis study investigates the impact of the size of the shadow economy on the performance of the financial and banking sectors in BRICS countries from 2002 to 2020. The countries included in this analysis are Brazil, Russia, India, China, Ethiopia, the United Arab Emirates, Iran, and Egypt. Given the characteristics of the data, a panel data approach is employed to estimate the models. To address both financial market development and banking sector performance simultaneously, a Seemingly Unrelated Regression (SUR) system within a panel data framework is utilized, representing a novel approach in this area of research. The use of panel SUR helps to mitigate the risk of obtaining misleading results due to sample heterogeneity. The models to be estimated for financial market development and banking sector performance are specified as follows: Model 1: Model 2: In these equations, FMI represents the financial market development index, FII denotes the performance index of financial institutions or the banking sector, and Shadow refers to the size of the shadow or hidden economy. HDI stands for the Human Development Index, GDPP indicates the growth rate of per capita gross domestic product, Inf represents the inflation rate, RR denotes natural resource rents, Kofgi is the globalization index, and Political signifies political stability. The subscript i refers to the country, while t indicates the time period. FindingsEmpirical analysis demonstrates that in BRICS countries, political stability exerts a positive influence on the composite indices of financial market and banking sector performance. Political stability creates an environment of economic certainty and predictability, thereby providing a long-term outlook for economic actors and investors. The impact on financial markets is notably stronger than its influence on banking sector performance. In resource-rich nations, however, these resources have not enhanced financial and banking efficiencies; instead, they have generated economic rents, fostered corruption, and weakened government functions. Growth in per capita income and the Human Development Index significantly and positively affects the performance of both financial markets and banking sectors. Economic growth provides a macroeconomic backdrop that stimulates demand for banks’ financial resources, as investors seek to channel these funds into profitable economic ventures. Moreover, globalization has a positive and statistically significant effect on financial market performance, while simultaneously posing a significant challenge for banks in the BRICS countries, revealing a negative and statistically significant impact on their performance. Additionally, inflation, which reflects the depreciating value of national currencies, considerably undermines the performance of both financial markets and the banking sector. Discussion and ConclusionThe findings highlight the detrimental impact of the shadow economy on the performance of financial markets and the efficiency of the banking sector. Controlling and reducing the size of informal economic sectors can enhance the functioning of financial markets and improve banks’ operational status. It is critical to monitor the allocation of financial and banking resources toward productive, formal economic activities while preventing their diversion into shadow sectors. Effective tax systems and regulations are essential for guiding financial resources toward the formal economy. The study also emphasizes the positive influence of institutional quality and political stability, advocating for policy frameworks aimed at enhancing transparency, ensuring economic predictability, stabilizing legislative environments, and prioritizing political stability to substantially improve the performance of the financial and banking sectors. Furthermore, policies that address inflation control and promote the transparent management of natural resource revenues are crucial. While globalization positively supports financial market development, it presents challenges for banking sector performance, necessitating a focus on banking conditions, structures, and regulatory frameworks within a global context as integral components of international cooperation efforts.
The importance of global markets and the growth of competition has led to a new division of work in the global and national economy. Such changes have caused attention to specific business issues such as obtaining resources, sharing information, and forming supply chain relationships for small companies and even large companies that try to form industrial parks. One of these changes is merger and acquisition, which is considered as a theoretical basis in the park formation. The present research has been carried out with the aim of providing a model to identify and apply factors that promote acquisition among the production units of a chemical park. The research method is descriptive-analytical, which uses various techniques of decision theory to determine the pattern of acquisition and ownership share of production units, financing and investment strategies, and the levels of integration and acquisition of the downstream units of the park . The application of the multi-criteria methodology and the design of the decision support system has made the park developer company's decision-making easier in the selection of production units, with the quantitative goals of the amount of ownership, acquisition and investment.
The primary objective of this research was to analyze the relative importance of working capital management factors in predicting financial distress among companies. The study population consisted of 167 companies listed on the Tehran Stock Exchange (TSE) from 2019 to 2023. 7 key working capital management indicators were selected based on their potential impacts on financial distress. Using Zavgren’s (1985) financial distress prediction model, the sample companies were classified into distressed and healthy groups. In the first step, a random forest algorithm was employed to assess the predictive power of the seven working capital management indicators in classifying companies as distressed or healthy. The results indicated that these indicators could successfully identify and predict the financial distress status of the companies with up to 85% accuracy. In the second step, the unique feature of the random forest algorithm was leveraged to rank the importance of each working capital component in achieving this 85% classification accuracy. The findings showed that the Average Collection Period (ACP) was significantly more important than the other working capital components in predicting financial distress.Keywords: Financial Distress, Working Capital Management, Average Collection Period (ACP), Random Forest AlgorithmJEL Classification: G01, G30, C38 IntroductionIn recent years, financial distress and bankruptcy have become increasingly prevalent issues for business enterprises. The financial literature offers various definitions to describe the state of financial distress and bankruptcy. While some researchers equate financial distress with bankruptcy, financial distress is more accurately viewed as a precursor to bankruptcy – a stage of financial decline that may or may not ultimately lead to a company's bankruptcy. Simply put, financial distress reflects a business entity's inability or weakness in fulfilling its obligations to creditors (Gerged et al., 2022). Given the rapid growth of joint-stock companies and the emergence of severe financial crises at both micro- and macro-economic scales, it is crucial to identify the key factors that can predict a company's financial health before it reaches the stage of bankruptcy, i.e., during the financial distress phase (Pourheydari et al., 2010). Evidence suggests that working capital management is a significant factor influencing the financial distress of business enterprises (Geng et al., 2015). Companies experiencing financial distress and bankruptcy often exhibit weaknesses in working capital management, particularly in cash control. Therefore, the aim of this study was to evaluate the predictive power of working capital management components in forecasting financial distress and rank the importance of each component in this prediction process.Materials & MethodsThe raw financial statement data for this research were extracted from Rahavard Novin Database and the Codal website. These data were then systematically organized in Excel. After applying certain eligibility criteria, a sample of 167 companies was identified as the accessible statistical population. To classify the sample companies into distressed and healthy groups, which served as the target variable (label), Zavgren’s (1985) financial distress prediction model was utilized. Subsequently, the predictive power of 7 key working capital management components in forecasting financial distress was tested using Python software and the random forest algorithm.The random forest method is based on ensemble learning, wherein the data are split into training and testing sets. During the learning phase, the model attempts to identify the inherent pattern or the relationship between the dependent variable (financial distress) and each explanatory variable (working capital management components) with the validity of this learning measured by the testing data. The random forest method employs a bagging approach, creating subsets from the entire dataset and determining the final result based on the average outcomes of these subsets. This approach helps to significantly mitigate the overfitting problem.One notable feature of the random forest algorithm is its ability to rank the importance of the input features in determining the trend of the target variables. This capability was leveraged in this research to answer the second research question, which focused on the relative importance of each working capital component in predicting financial distress. Research FindingsThe model achieved an accuracy of 85%, indicating that it could correctly predict whether a company was in financial distress or not based on what it learned during the training phase. Additionally, the model's F1-Score metric was 0.89 for identifying healthy companies and 0.76 for predicting distressed companies. These scores, being close to 1, suggested that the model's estimations were performed with a high degree of accuracy.The analysis of the relative importance of each working capital management component in achieving this 85% accuracy rate revealed some key insights. The Average Collection Period (ACP) was identified as the most important factor in predicting financial distress. Following the ACP, the Current Ratio (CR) ranked second, the Average Payable Period (APP) ranked third, and the Inventory Turnover In Days (ITID) ranked fourth in importance.These findings suggested that the initial signs of financial trouble for a company often stemmed from its failure to collect receivables in a timely manner, leading to an increased collection period. If the company's management did not effectively address this issue, other problems could likely arise, ultimately pushing the business entity into a state of financial distress. Discussion of Results & ConclusionThe results of the data analysis using the random forest algorithm indicated that working capital management indicators had an 85% predictive power for identifying financial distress in companies. This finding is consistent with those of the previous studies by Habib and Kayani (2022), Morshed (2020), and Li et al. (2018). Regarding the second research objective, which aimed to rank the importance of each working capital management component in predicting financial distress, the analysis revealed that the Average Collection Period (ACP) was the most significant factor. This suggested that a company's inability to collect receivables in a timely manner was a crucial early indicator of impending financial distress.An increase in the ACP could lead to a serious risk of bad debts and liquidity problems for the company. As a result, the company's management might need to secure additional working capital to fund operations, which could potentially increase the Weighted Average Cost of Capital (WACC). However, if the company failed to generate adequate returns to cover these elevated financing costs, it might ultimately fall into a state of financial distress (Panigrahi, 2014). Given the notable importance of the ACP compared to other working capital management components, it appeared that many of the underlying issues leading to financial distress stemmed from poor performance in collecting receivables. Therefore, this research underscored the critical need for robust management practices of receivables to maintain liquidity and avoid the escalating costs and risks associated with financial distress.
The financing policies implemented by managers play a pivotal role in risk management and shareholder wealth creation. Consequently, identifying the factors that influence managerial financing decisions is critically important. This study examines the impact of managerial ability on short-term debt usage, incorporating the moderating effects of financial constraints and financial reporting quality. The sample includes 100 firms listed on the Tehran Stock Exchange, selected through systematic elimination for the period 2012–2023. A multivariate regression model based on panel data analysis was employed to test the hypotheses. The findings demonstrate that managerial ability has a positive effect on debt maturity. Additionally, while financial constraints do not significantly moderate this relationship, financial reporting quality strengthens the influence of managerial ability on short-term debt utilization. Specifically, high-ability managers—equipped with superior business acumen and strong incentives to signal their competence—tend to employ greater short-term debt to mitigate information asymmetry and enhance their reputational capital.Keywords: Debt Structure, Managers' Ability, Financial Constraints, Financial Reporting QualityJEL Classification: M40, H63, D04, M41 IntroductionDebt financing is a fundamental component of corporate capital structure, playing a crucial role in firm sustainability and growth. The composition of debt—particularly its maturity structure—serves as a key determinant of financial stability and long-term success. Consequently, decisions regarding debt structure are critical, as misjudgments can expose firms to financial distress or even bankruptcy. Prior research has examined various determinants of debt maturity structure, including macroeconomic and institutional factors such as financial and political environments, legal and tax systems, information asymmetry, and capital provider characteristics. Another stream of literature focuses on firm-specific influences, particularly managerial traits, given their significance in mitigating agency conflicts between shareholders and managers. Among these traits, managerial ability stands out as a pivotal factor shaping debt maturity decisions. Aligned with theoretical foundations, this study proposes the following hypotheses:H₁: Managerial ability positively influences debt maturity.H₂: Financial constraints attenuate the effect of managerial ability on debt maturity.H₃: Financial reporting quality amplifies the impact of managerial ability on debt maturity.Materials & Methods and dataThe study examines firms listed on the Tehran Stock Exchange (TSE) over the period 2012–2023. The sample was selected through systematic elimination to ensure data integrity and representativeness. To test the hypotheses, we employed panel regression analysis using the Generalized Least Squares (GLS) estimator, which accounts for heteroskedasticity and autocorrelation in the data. Managerial ability was operationalized following Demerjian et al. (2012), while financial reporting quality was measured using the Dechow and Dichev (2002) accruals quality model. FindingThe empirical results demonstrate several key insights. As presented in Table 2, managerial ability exhibits a statistically significant positive relationship with firms' utilization of short-term debt. This finding aligns with theoretical expectations, as short-term debt instruments can serve as effective mechanisms to mitigate information asymmetry between managers and investors. Moreover, the preferential use of short-term debt may function as a positive market signal, conveying managers' confidence in the firm's near-term financial prospects. Table 3 reveals that financial constraints do not significantly moderate the relationship between managerial ability and debt maturity structure. This suggests that capable managers maintain their influence over financing decisions regardless of external financial limitations. Finally, Table 4 presents evidence that financial reporting quality strengthens the positive association between managerial ability and short-term debt usage. This amplification effect likely occurs because high-quality financial reporting enhances transparency, thereby increasing the credibility of managers' financing decisions. Discussion and ConclusionCorporate financing decisions are predominantly shaped by managerial discretion, with short-term debt instruments gaining increasing prominence over the past three decades. Our findings align with signaling theory, which posits that short-term debt issuance serves dual purposes: it reduces information asymmetry while simultaneously functioning as a positive market signal of managerial competence. Conversely, agency theory would predict an inverse relationship, suggesting that higher managerial ability might correlate with reduced short-term debt due to inherent agency conflicts in firms where managerial capabilities are less observable. The empirical evidence supports the signaling perspective, demonstrating that high-ability managers strategically utilize short-term debt to distinguish themselves from their less competent counterparts. This behavior stems from their superior capacity to assess market conditions and capitalize on favorable financing opportunities. Furthermore, our analysis reveals that managerial ability plays a particularly significant role in firms with higher reporting quality. In such organizations, which typically possess more robust project portfolios, short-term debt issuance serves as an additional quality indicator. High-ability managers in these firms are more inclined to employ short-term debt instruments, thereby reinforcing their reputation for financial acumen and strengthening market confidence. These findings contribute to the ongoing theoretical discourse by reconciling competing perspectives from signaling and agency theories. They also offer practical implications for corporate governance, suggesting that boards should consider managerial ability as a key factor in financing policy decisions, particularly in firms with transparent financial reporting environments.
The stock exchange serves as a critical source of corporate financing and as a platform for individuals to invest their savings, attracting a substantial amount of domestic capital in recent years and playing a pivotal role in the country's economic growth and development. This study investigates herding behavior in the Tehran Stock Exchange (TSE) under various economic and social conditions, including periods before and after exchange rate fluctuations, prior to and following the COVID-19 pandemic, and during both bullish and bearish market phases from April 2015 to March 2023. The analysis is based on the overall stock price index and the price index of the top 50 companies, employing Ordinary Least Squares (OLS) regression and quantile regression methodologies. The findings indicate that herding behavior is significantly evident in the TSE throughout the entire study period and across most quantiles (from the 0.05 to the 0.75 quantile) for both indices. Specifically, herding behavior is prominently observed prior to significant exchange rate fluctuations; however, it becomes unconfirmed afterward due to the insignificance of the resulting coefficients. Furthermore, herding behavior was found to be present in the TSE before the COVID-19 outbreak, with a notable decline after the pandemic, and in some cases, a reversal was observed. The analyses also demonstrate that herding behavior persists in both bullish and bearish market conditions, especially within the lower market quantiles.Keywords: Herding Behavior, Quantile Regression, COVID-19 Pandemic, Exchange Rate Fluctuations, Bullish and Bearish MarketsJEL Classification: F65, C32, E44 IntroductionThe capital market serves as one of the fundamental pillars of the economy, playing a vital role in fostering economic growth and development. In recent years, Iran’s capital market has attracted significant attention from both traders and policymakers, owing to its financial appeal and investment opportunities. However, irrational and emotional behaviors among investors within this market have presented substantial challenges. A major issue is herding behavior, which refers to the innate human tendency to mimic others. In financial markets, such behavior can drive investors to make irrational decisions and engage in high-risk transactions, as participants often base their choices not on the intrinsic value of stocks, but rather on the perceived actions and expectations of others regarding future price movements (Bikhchandani & Sharma, 2000). Given the critical role of the capital market and its investors as key players in the economy, it is essential to examine their behavior to promote optimal decision-making and ensure proper market functioning. This study utilizes behavioral finance theories and analyzes data related to TSE to investigate investor behavior through the lens of herding behavior. Specifically, the research explores herding behavior under various macroeconomic conditions, including currency fluctuations, both bearish and bullish market environments, and the COVID-19 pandemic as a unique social context. Materials & Methods To evaluate herding behavior, Christie and Huang (1995) and Chang et al. (2000) employed modeling approaches based on the cross-sectional dispersion of stock returns. The methodologies in both studies are grounded in the principle that, when herding behavior is present, individual stock returns tend to converge toward the overall market return. As a result, herding behavior leads to minimal differences between individual stock returns and the market return index. These minor discrepancies are quantified using the cross-sectional standard deviation (CSSD) and the cross-sectional absolute deviation (CSAD). Given the limitations of the CSSD model—such as the necessity of estimating excess returns and its inability to account for potential herding behavior during stable periods—this study adopts the CSAD model to address these shortcomings. The CSAD model enhances the CSSD framework by incorporating cross-sectional absolute deviations, providing a more robust analysis of herding behavior.Additionally, in light of the nature of the research problem, this study employs quantile regression (QR), introduced by Koenker and Bassett (1978), to conduct a more nuanced analysis of herding behavior. Quantile regression provides a more precise methodology by capturing variations across the entire distribution of the dependent variable and addressing the limitations of ordinary least squares (OLS) estimators (Barnes & Hughes, 2002; Zhou & Anderson, 2013). This approach models the response of the dependent variable to the independent variable at various quantiles, denoted as "τ," making it an effective tool for analyzing non-normal distributions. Moreover, quantile regression is particularly adept at handling outliers, extreme values, and non-normal deviations (Xiao, 2012; Allen et al., 2013; Alexander, 2008). FindingsThe results of the OLS along w indicate the presence of herding behavior in the Tehran Stock Exchange (TSE) throughout the study period. This finding reflects a tendency among investors to engage in collective behavior in the TSE across various time intervals. Further confirmation of herding behavior is provided by the quantile regression (QR) results, which reveal its presence in the lower and middle quantiles of the stock market during the study period, particularly under normal market conditions. However, in the higher quantiles, herding behavior diminishes and, in some cases, even reverses. This suggests that investors in the upper quantiles are more inclined to make independent decisions and are less likely to follow the crowd.During periods of exchange rate fluctuations, herding behavior was notably observed prior to sharp increases in exchange rates. These findings imply that in the lead-up to significant economic volatility, investors, driven by uncertainty regarding future conditions, are more likely to engage in herding behavior. However, after experiencing intense currency fluctuations, while some indications of herding behavior persisted, they were not statistically significant. This outcome may suggest that following substantial volatility, investors tend to adopt more individualized and potentially more conservative strategies. The QR analysis also indicated that herding behavior was more pronounced in the lower quantiles of the market, which may reflect the influence of exchange rate fluctuations on less risk-tolerant investors.During the COVID-19 pandemic, the study results revealed the presence of herding behavior in both the overall index and the top 50 companies’ index prior to the pandemic. This finding is noteworthy, as it indicates that herding behavior was evident in the TSE even before the onset of a global crisis. However, following the pandemic, herding behavior significantly declined and, in some instances, reversed. These changes underscore the impact of crisis conditions and increased volatility on investor behavior, suggesting that, in critical situations, investors tend to rely more on independent decision-making and individual assessments. The results from both the OLS and QR analyses further indicated the presence of herding behavior in TSE during both bullish and bearish market conditions, particularly in the lower quantiles of the market. In bullish markets, investors typically gravitate toward purchasing stocks with positive returns, while in bearish markets, they are inclined to sell stocks with negative returns. These behaviors, especially prevalent in the lower quantiles, clearly illustrate the sensitivity of investors to market conditions and their propensity to follow the crowd. Conclusion and DiscussionThis study analyzed herding behavior in the Tehran Stock Exchange (TSE) from 2015 to 2022 using Ordinary Least Squares (OLS) and quantile regression methods, examining both the overall index and the index of the top 50 companies. The results revealed the presence of herding behavior throughout the study period and under various economic and social conditions, including exchange rate fluctuations, the COVID-19 pandemic, and both bullish and bearish markets. However, the intensity of herding behavior varied depending on the circumstances, with a stronger presence observed in the lower quantiles of the market. Following periods of severe exchange rate volatility and the pandemic, a noticeable shift toward independent behavior and individual decision-making emerged. Furthermore, herding behavior was identified as a contributing factor to market volatility. To enhance the efficiency of the stock market and mitigate the negative effects of herding behavior, it is recommended to improve information transparency and provide investors with access to independent analyses. Effective solutions include offering education in technical and fundamental analysis, strengthening oversight and regulations, diversifying financial instruments such as exchange-traded funds (ETFs), and adopting stable monetary and fiscal policies. Additionally, developing IT infrastructure, introducing tax incentives for long-term investments, and fostering collaboration among related institutions can further contribute to the sustainability and stability of the market.
This study employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) model to investigate the dynamic relationships among various commodity markets—including copper, aluminum, nickel, tin, zinc, lead, gold, and crude oil—and the Iranian stock market over the period from June 16, 2014, to May 21, 2024. The results indicate that cross-market interactions account for approximately 42.69% of the forecast error variance, revealing significant spillovers among these markets. By utilizing pairwise connectedness indices, an optimal asset portfolio is constructed using the minimum connectedness approach (MCoP) and subsequently compared with traditional methods such as the Minimum Variance Portfolio (MVP) and the Minimum Correlation Portfolio (MCP). The results demonstrate that optimal portfolio weights vary across investment strategies, with gold and the Iranian stock index consistently exhibiting the highest weights in the optimal portfolios. Furthermore, an examination of optimal weights in two-asset portfolios highlights a preference for increased investments in copper and gold. Hedging strategies also prove effective in mitigating asset volatility, particularly in the nickel and Brent oil markets.Keywords: Connectedness Approach, Risk, Market Spillover, Portfolio Management. IntroductionRecently, the integration of the global financial system has significantly declined due to external shocks, underscoring the critical need for diversification in investment portfolios. The Markowitz Portfolio Theory serves as the foundational framework in this context, emphasizing the simultaneous evaluation of risk and return in investment decisions. Its primary objective is to identify a portfolio that minimizes risk for a specified level of return or maximizes return for a given level of risk. Furthermore, the Minimum Correlation approach calculates the weights of the investment portfolio using a conditional correlation matrix. A more contemporary method, known as Minimum Connectedness, has gained prominence as it allocates greater weights to assets that exhibit minimal connectivity and influence on other assets. This method assesses the interdependencies among assets and the spillover effects between markets. Despite the extensive body of research examining spillovers across various markets, the specific analysis of spillovers between the Iranian stock market and commodity markets has not been sufficiently explored. This study aims to address this gap in the literature. Materials and MethodsThis study employs a four-step empirical framework. In the first stage, the Time-Varying Parameter Vector Autoregression (TVP-VAR) method is utilized to analyze the dynamic relationships among essential metals, including copper, aluminum, nickel, tin, zinc, and lead, as well as gold, oil, and the Tehran Stock Exchange Index, during the period from June 16, 2014, to May 21, 2024. The TVP-VAR method is particularly well-suited for examining complex and dynamic financial relationships due to its unique characteristics, such as the elimination of random window selection requirements and reduced sensitivity to outlier observations. The second phase involves variance decomposition, which facilitates a more nuanced analysis of the connections and spillovers among the markets. In the third phase, an optimal asset portfolio is constructed using the Minimum Connectedness approach (MCoP), and its performance is compared with that of traditional methods, including the Minimum Variance Portfolio (MVP) and the Minimum Correlation Portfolio (MCP). Finally, two key performance measures—hedging effectiveness and cumulative return—are employed to evaluate portfolio performance. These analytical stages are conducted through both pairwise and multivariate analyses to enhance the robustness and accuracy of the results. FindingsThe results ofg the TVP-VAR model indicate that the average total connections amount to 69.42, accounting for approximately 69.42% of the forecast error variance, while in-sample variations represent the remaining 31.57%. These findings suggest a relatively strong systemic risk among the studied markets. Within this network, copper exhibited the highest spillover effect to other markets, whereas Brent oil and gold demonstrated significant responsiveness. Throughout the observation period, copper consistently maintained the highest level of connectivity among the base metals. The estimation of optimal weights revealed considerable variation in asset allocations across different investment approaches. Notably, gold held the highest weight in the Minimum Variance Portfolio (MVP) approach, while the Iranian stock index represented the largest weight in the other two portfolio strategies. The discrepancies in optimal weights within the MVP approach were more pronounced compared to the other two approaches, which exhibited greater similarity in their asset allocations. Furthermore, hedging effectiveness across all three approaches demonstrated that hedging strategies—particularly for nickel and Brent oil—significantly reduce asset volatility. An analysis of optimal weights in two-asset portfolios indicated greater average allocations for the Copper/Nickel and Gold/Brent pairs, highlighting a tendency for increased investment in copper and gold. Additionally, the high hedging effectiveness of 91% in the Nickel/Gold portfolio suggests that incorporating nickel into the investment strategy can enhance risk-return characteristics. The optimal hedging ratios illustrated the highest risk neutralization for the Nickel/Copper combination, achieving an average efficiency of 84%, while the ratio for Nickel/TSE was negative, with a minimum value of -0.04. Discussion & ConclusionThe analysis highlights the essential role of diversification within asset portfolios for effective risk management. The findings emphasize the significant weight attributed to copper and gold across various investment strategies. Copper, due to its substantial spillover effects on other markets, and gold, which serves as a safe-haven asset resilient to market fluctuations, have been identified as strategic investments. Consequently, it is advisable for investors to closely monitor these two markets and increase their allocations in investment portfolios to enhance risk-return characteristics. Moreover, hedging strategies provide investors with the capability to mitigate existing asset volatilities and associated risks. This research underscores the necessity for continuous and adaptable portfolio management, particularly in light of the dynamic nature of correlations among assets. Overall, employing innovative approaches, such as Minimum Connectedness, in lieu of traditional methods may lead to improved investment portfolio performance and allow for quicker responses to market changes. This adaptability and dynamism in portfolio management ultimately contribute to reduced risk and increased returns.
While audit quality remains one of the most widely examined topics in auditing research, its influence on corporate investment behavior and financing decisions remains underexplored. This study examines the dual impact of audit quality on organizational capital and financing capacity using data from 148 Tehran Stock Exchange (TSE) listed firms (2011-2022). Employing audit fees as an audit quality proxy within an Analytical Hierarchy Process (AHP) framework, we find audit quality significantly enhances organizational capital, suggesting high-quality audits facilitate strategic resource allocation and capital formation. Conversely, we document an inverse relationship between audit quality and financing capacity, revealing a dynamic interaction where rigorous auditing may initially constrain but ultimately strengthen firms' financial sustainability by improving credibility and access to stable funding sources. These findings contribute to the auditing literature by demonstrating audit quality's dual role as both an enabler of organizational development and a moderator of financial constraints.Keywords: Audit Quality, Organizational Capital, Audit Quality Metrics, Financing Capacity JEL Classification: M42, O16, G32 IntroductionThis study examines the dual role of audit quality in fostering organizational capital development and enhancing corporate financing capacity, addressing a significant gap in the literature regarding audit quality's influence on intangible investments. While prior research has established audit quality's importance in improving financial reporting transparency and reducing information asymmetry (DeFond & Zhang, 2014), its impact on strategic organizational assets remains underexplored. Organizational capital - the synergistic combination of knowledge, human capital, and physical assets - serves as a critical driver of value creation and competitive advantage (Georgantopoulos et al., 2022). We hypothesize that high-quality audits positively influence organizational capital by facilitating strategic investments (H1) and exhibit a complex, bidirectional relationship with financing capacity (H2), particularly valuable in economically volatile environments where access to external financing proves challenging (Lim et al., 2022). By analyzing Tehran Stock Exchange-listed firms, this study provides novel insights into how audit quality serves as an institutional mechanism that simultaneously nurtures intangible assets and improves financial access, thereby contributing to both corporate finance theory and practice. The findings offer meaningful implications for regulators and firms seeking to optimize their audit investments for both organizational development and financial sustainability. Metod and dataThis study employs panel data analysis of 1,776 firm-year observations (148 companies) from Tehran Stock Exchange-listed firms between 2011-2022, sourced from the Codal database and the Management Research, Development, and Islamic Studies Library. Following rigorous screening of the initial 5,496 observations from 458 companies, we implemented modified versions of Georgantopoulos et al.'s (2022) econometric models to examine audit quality's dual impact on organizational capital and financing capacity. Our methodological approach incorporates (1) multivariate regression analysis to assess the hypothesized relationships, (2) robustness checks to address potential endogeneity concerns, and (3) industry-adjusted measures to control for sector-specific variations. The selected models specifically account for firm-level characteristics while controlling for macroeconomic factors prevalent in emerging market contexts. Model (1):OCit=α+β1AQit+β2SIZEit+β3DEBTit+β4TOBINQit+β5FCFit+β6NCTit+β7GROWTHit+β8ISSUEit +β9SUBSit+β10ROAit+β11IOWNit+β12BINDit+β13YEARDUMit+β14INDDUMit +€it Model (2):FCit=α+β1AQit+β2SIZEit+β3DEBTit+β4TOBINQit+β5FCFit+β6NCTit+β7GROWTHit+β8ISSUEit+β9SUBSit+β10ROAit+β11IOWNit+β12BINDit+β13YEARDUMit+β14INDDUMit +€it Our measurement approach operationalizes organizational capital (OC) following Peters and Taylor's (2017) Model (3): ORGC it =(1-y0) ORGCit-1 +(SG&Ait * ð0) where y represents the depreciation rate, SG&Ait denotes total selling, general, and administrative expenses, and δ reflects the proportion of training costs to total SG&A expenses. Financing capacity (FC) is calculated as the annual interest expense-to-total debt payments ratio. To address audit quality measurement complexities, we employed the Analytical Hierarchy Process (AHP), administering a pairwise comparison matrix to 15 auditing scholars and practitioners. The consistency evaluation (consistency ratio < 0.1) identified audit fees (AQ_AF) as the optimal proxy, given its superior weighting score (0.72) across relevance, reliability, and data availability criteria. FindingsOur empirical results support the first hypothesis, revealing a statistically significant positive relationship between audit quality and organizational capital (β = 0.04, p < 0.01), suggesting that each unit increase in audit fees corresponds to a 0.04-unit increase in organizational capital. This finding aligns with the theoretical expectation that high-quality audits contribute to knowledge accumulation and strategic decision-making enhancement through rigorous verification processes. Regarding the second hypothesis, we observe a significant negative association between audit quality and financing capacity (β = -0.12, p < 0.05), supporting the substitution effect hypothesis where firms facing financial constraints demand higher audit quality to compensate for increased information asymmetry. The results suggest an inverse dynamic equilibrium where improved financing capacity reduces the marginal benefit of audit quality, consistent with agency cost theory predictions. These findings collectively demonstrate audit quality's dual role as both an organizational capital enhancer and a financial constraint mitigator. Discussion and ConclusionThis study reveals two key findings regarding audit quality's dual role: first, audit fees (as an audit quality proxy) exhibit a significant positive relationship with organizational capital (β=0.42, p<0.01), supporting the proposition that high-quality audits facilitate knowledge transfer and strategic investment in intangible assets, consistent with Georgantopoulos et al.'s (2022) findings. Second, we identify a significant negative association between audit quality and financing capacity (β=-0.31, p<0.05), confirming that firms facing financial constraints demand higher audit quality to mitigate information asymmetry and improve resource access, aligning with McNelly et al. (2019) and AlaviTabari and Hashemiyan (2011). These results collectively advance our understanding of audit quality's dual function as both an organizational capital enhancer and financial constraint moderator. The study contributes to the literature by empirically validating these relationships in an emerging market context, while suggesting future research avenues to examine audit quality's impact on other intangible assets like human capital within this framework.
This study aimed to identify, extract, and classify the factors and requisites influencing the development of Real Estate Investment Trusts (REITs) using a qualitative research method. A systematic review was conducted, examining 1,869 relevant studies published over 23 years (2000-2023). After applying various filters, 91 studies most closely aligned with the objectives were selected for final analysis. The factors and requisites for REIT development were then classified into 4 overarching and 12 organizing themes. The findings indicated that the factors and requisites for REIT development could be analyzed across 4 broad categories: functional, supervisory and supportive, infrastructural, and structural and governance. The functional aspects included attention to financial and economic indicators, operational processes, investment principles, and risk management. The supervisory and supportive aspects covered legislation, policymaking, and regulatory frameworks. The infrastructural aspects encompassed cultural, educational, innovative, and technological dimensions. The structural and governance aspects involved monitoring of managers, investors, and corporate governance. The use of the meta-synthesis approach in this research ensured a comprehensive and holistic review of both domestic and international studies in this field, facilitating the identification and categorization of the multifaceted dimensions influencing REIT development.
The primary objective of a Venture Capital (VC) firm is to achieve investment success and profitability, particularly given the challenges and high failure rates associated with investments in startup companies. This study aimes to establish a framework of Key Success Factors (KSFs) for VC firms operating within Iran's innovation ecosystem. Additionally, it offered a comprehensive methodology for assessing these factors. To achieve this, we employed a meta-synthesis method to build upon the research conducted by Rashidi et al. (2023). After searching and refining both English and Persian sources, we conducted 12 interviews with VC experts. Through thematic analysis and re-categorization of primary codes, we identified a total of 139 basic themes, which were further organized into 22 organizing themes and 11 comprehensive themes, all of which were structured under 3 macro themes. Subsequently, a questionnaire was presented to experts and the DEMATEL technique was utilized to identify the significant relationships between the organizing themes. Finally, using the TOPSIS method, we prioritized the factors selected by the experts, identifying 5 KSFs for VC firms in Iran: "investing in motivated and capable founders", "establishing communication networks within and outside the invested industry", "ensuring trust and honesty between founders and the VC firm", "entering large and growing markets", and "developing the soft skills of VC firm managers".Keywords: Venture Capital (VC), Key Success Factors (KSFs), Entrepreneurial Finance, TOPSIS, DEMATEL. IntroductionVenture Capital (VC) firms operate with an indefinite lifespan and manage multiple portfolios under their umbrella. VC investors are experienced as financial and business professionals, who provide both financial and managerial support to the most innovative and promising companies. In the relationship between entrepreneurs and VC investors, these investors do not merely serve as providers of financial resources; they actively contribute to the growth of companies by engaging in management, strategic marketing, and business planning. The success of VC firms is influenced by a variety of factors and achieving this success often appears complex and elusive. Identifying the Key Success Factors (KSFs) for VC firms is essential for conducting an internal environment analysis during strategic planning. This identification not only establishes a basis for measuring the performance of these firms, but also enhances the focus and intelligence of investments at a macro level. This study aimed to present a framework of KSFs for VC firms within Iran’s innovation ecosystem and offer a comprehensive method for identifying these factors. Specifically, this research sought to answer two key questions: first, "What indicators define the success of VC firms?" and second, "What factors contribute to the success of VC firms?" A review of the literature indicated that previous research had addressed aspects of these questions. Therefore, this study, building on the findings of Rashidi et al. (2024), aimed to complete the framework of KSFs for VC firms. Materials & MethodsThis study employed a mixed-methods research design. Initially, the "meta-synthesis" method, along with "interviews", was used to identify the success factors and indicators for VC firms. Following this, a thematic network of these factors was developed through "thematic analysis", resulting in a preliminary comprehensive model of the success factors and indicators relevant to VC firms. In the next phase, the themes identified through thematic analysis were ranked based on responses from VC experts, utilizing completed DEMATEL questionnaires. This process determined the influence, impact, and significance of each category of success factors. Finally, to establish the KSFs for VC firms, several fundamental themes endorsed by experts were analyzed using the TOPSIS method. Through expert feedback, a two-dimensional matrix of option-indicators was created, ultimately identifying 5 KSFs for VC firms operating in Iran.FindingsThe results obtained from the interview process and meta-synthesis were categorized through thematic analysis. In response to the first research question, 23 organizing themes and 11 global themes were analyzed and classified into 3 broad categories: "Characteristics Related to the Investee", "Internal Characteristics of the VC Firm", and "Environmental Characteristics". From these basic themes, 22 options were selected and refined for inclusion in the TOPSIS process based on expert input. For the second research question, 2 main themes and 11 sub-themes were extracted. The main themes, "Financial Return" and "Strategic Impact", served as input indicators for the TOPSIS analysis. Based on the TOPSIS output, the top 5 factors influencing the success of VC firms in Iran, in order of significance, were: 1) investing in motivated and capable founders, 2) establishing a network of connections within and outside the target industry (including governmental institutions), 3) fostering trust and transparency between the founder(s) and the VC firm, 4) entering large and scalable markets, and 5) developing the soft skills (such as negotiation, leadership, and teamwork) of VC firm managers. Additionally, the DEMATEL technique indicated that the most influenced success factor was the "Investment Strategy of the VC Firm", while the most influential factor was the "Resources and Connections of the VC Firm", which was also affected by other factors. Furthermore, the "Investment Strategy of the VC Firm" displayed the highest degree of interconnection with other factors. Discussion & ConclusionThis study aimed to comprehensively identify the success factors for VC firms by conducting interviews with industry professionals and systematically reviewing the existing research. Utilizing soft Operational Research (OR) methods, we analyzed the relationships between these factors and their prioritization concerning success indicators. The review of KSFs for VC firms in Iran revealed a strong emphasis on Human Resource (HR) and interaction-based perspectives. Notably, 4 of the KSFs focused significantly on HRs and interpersonal skills—an aspect often overlooked by investors, particularly those with backgrounds in finance and capital markets. Ultimately, this research built upon prior models to present a new framework for identifying KSFs specific to the VC industry, which may also inform future research. This framework encompassed reviewing the existing literature, conducting interviews with industry experts, refining important success factors, prioritizing these factors, determining the KSFs, and evaluating the relationships between these factors to support strategic formulation within organizations.
External transparency extends beyond the quality of internal disclosures, which are primarily governed by laws and regulations. This dimension of transparency encompasses external requirements and pressures that compel managers to adhere to higher standards of information disclosure. This study examines the impact of external transparency on corporate social responsibility (CSR) performance and disclosure. This study analyzes data from 105 companies listed on the Tehran Stock Exchange between 2013 and 2022, using EViews and Stata software. The findings reveal that heightened external transparency enhances both CSR performance and disclosure. External transparency pressures foster greater corporate transparency, thereby improving CSR disclosures. Additionally, increased transparency mitigates information asymmetry and agency problems, aligning managerial objectives with corporate goals and ultimately enhancing CSR performance. This study contributes to the literature by demonstrating that external transparency serves as a robust predictor of CSR activities. Moreover, it highlights the role of external transparency in encouraging managers to produce more comprehensive CSR reports. The research also uncovers policy implications, illustrating how external transparency pressures drive firms toward greater social responsibility.Keywords: External Transparency, External Pressures, Performance of Social Responsibility, Disclosure of Social ResponsibilityJEL Classification: D25, D53, M41 IntroductionAlthough prior research has made significant progress in exploring the relationship between transparency and social responsibility, gaps remain in understanding how information asymmetry affects CSR performance. Existing literature presents mixed findings regarding transparency’s impact on CSR. Some studies suggest that transparency may reduce CSR investments due to short-term performance pressures (Aguinis & Glavas, 2012; Margolis & Walsh, 2003; Orlitzky et al., 2017), as noted by Fiesler (2011). Conversely, other research indicates that increased transparency may enhance CSR investments by attracting more analysts and bolstering corporate reputation (Luo et al., 2015; Gao et al., 2016). Studies also suggest that external pressures may incentivize firms to prioritize CSR activities to align with societal expectations (Garcia Sanchez et al., 2021). However, the literature remains inconclusive on whether transparency increases or decreases CSR investments.Prior research has predominantly examined transparency from an analyst’s perspective, whereas external stakeholder pressures compel managers to meet shareholder expectations and ensure financial performance (Pondville et al., 2013; Rowley & Berman, 2000). Anderson et al. (2009) categorize transparency into internal (disclosure quality) and external (market scrutiny), with the latter necessitating clearer information disclosure. External transparency, driven by external pressures, may influence CSR performance and disclosures—a relationship this study seeks to explore (Bushman & Smith, 2003).Methods & MaterialsThe data for this study were collected from multiple sources, including the Tehran Stock Exchange database, the Tehran Stock Exchange Technology Management Company, and the Tehran Stock Exchange Library, which provided variables related to external transparency, bid-ask spread, and trading volume. Additional data for control variables were extracted from Rahavard Novin software, financial statements, and company notes, while the Board of Directors’ activity reports to the General Assembly of Shareholders supplied information on CSR and corporate governance quality.The sample comprises companies listed on the Tehran Stock Exchange from 2013 to 2022. Applying specific selection criteria, a sample of 105 firms was selected, yielding 1,050 firm-year observations. Preliminary data processing was conducted in Excel, while final analyses were performed using EViews (version 13) and Stata (version 17). FindingsRegression model estimations indicate a positive and significant relationship between external transparency and both CSR performance and disclosure. The first hypothesis, examining the effect of external transparency on CSR performance, was confirmed, suggesting that increased transparency enhances CSR performance. The second hypothesis, tested via logistic regression, also confirmed a positive and significant association between external transparency and CSR disclosure, indicating that greater transparency leads to more robust CSR disclosures.Existing literature suggests that external stakeholder pressures for transparency help bridge the gap between disclosed and actual performance, preventing misleading CSR reporting (Anderson et al., 2009). Market expectations and oversight compel firms to present information more clearly (Leuz, 2000). Such monitoring pressures encourage firms to make more informed CSR decisions and better assess risks (Bushman et al., 2004). External transparency surpasses internal disclosure quality, which is often legally mandated, by incorporating external pressures that push managers toward higher disclosure standards (Bushman & Smith, 2003). As transparency pressures intensify, firms shift focus toward long-term performance, whereas reduced pressures may lead to short-termism rooted in agency theory. Discussion and ConclusionThe findings align with prior research, demonstrating that external transparency positively influences CSR disclosure and performance. These results suggest that external pressures for transparency foster a more transparent informational environment, thereby improving CSR disclosures. Additionally, heightened transparency reduces information asymmetry and agency conflicts, aligning managerial and corporate objectives, which in turn enhances CSR performance.In Iran’s current economic climate—marked by sanctions and currency fluctuations—firms face elevated CSR-related risks. External transparency can serve as a critical tool in mitigating information asymmetry in financial markets, enabling firms to strengthen their market position through improved disclosure practices.
Corporate financialization has attracted significant attention from researchers due to its implications for financial stability, economic growth, and income inequality. This phenomenon is profoundly influenced by economic uncertainty and the financial constraints faced by firms. The primary objective of this study was to investigate how financial constraints moderated the relationship between economic policy uncertainty and corporate financing decisions. The sample comprised 125 companies listed on the Tehran Stock Exchange (TSE). Appropriate regression analyses were conducted following preliminary tests. The findings indicated that economic policy uncertainty had led to a substitution effect between business investment and financial investment. Furthermore, financial constraints were identified as a moderating factor in the relationship between economic policy uncertainty and corporate financing decisions. Additionally, different types of firms demonstrated varying levels of sensitivity and responsiveness to economic policies under diverse economic conditions. Keywords: Company Financialization, Economic Policy Uncertainty, Financial Constraints. JEL Classification: D04, D81, G38 Introduction In recent years, the real economy has faced unprecedented pressures and risks, resulting in declining investment returns. Concurrently, financial development has increasingly diverged from its role of supporting the real economy, leading to an accumulation of funds within the financial system (Tang & Zhang, 2019). Various factors influence corporate financing; however, these factors cannot be detached from the broader macroeconomic environment. Governments often implement economic reform policies that heighten uncertainty regarding economic policies and increase volatility in financial markets. This raises a critical question: How does economic policy uncertainty influence the allocation of financial assets by companies and how does this, in turn, promote corporate financialization? Previous studies have shown that companies frequently adjust their investment strategies during periods of financial crisis and heightened uncertainty surrounding economic policies (Durnev, 2010). While existing literature has explored the effects of economic uncertainty on various business decisions, such as increasing cash holdings, reducing capital expenditures, and engaging in merger and acquisition activities, there is a notable gap in research specifically addressing the impact of economic uncertainty on corporate financing (Nguyen & Phan, 2017; Gulen & Ion, 2016). Recent empirical studies emphasize the critical role of financial constraints in moderating the effects of economic policy uncertainty on corporate financing. Firms facing financial constraints, such as limited access to external financing, may exhibit heightened sensitivity to economic policy uncertainty, leading to a greater reliance on financing as a risk mitigation strategy (Chun et al., 2023). This study aimed to enhance the existing literature on economic policy uncertainty from the perspective of corporate financing while providing new empirical evidence to elucidate the U-shaped relationship between economic policy uncertainty and corporate financing in emerging markets. Materials & Methods To determine the appropriate model for estimating the research framework, the Hausman and Limmer tests were employed, while the Breusch-Pagan test was used to assess heteroscedasticity. The Jarque-Bera test was applied to evaluate the normality of the residuals. To establish the reliability of the research variables, the Levin, Lin, and Chu tests were conducted. Given the significance of the variables, it could be concluded that the regression models designed for hypothesis testing were valid. The independent variable in this study was economic policy uncertainty defined as a situation in which the probabilities of future events are indeterminate; even when potential events are known, their associated probabilities remain uncertain. Following the methodologies of Demir (2009), Tang and Zhang (2019), and Zhou and Guo (2021), this study measured firm financialization using the ratio of financial assets held by firms (i.e., the ratio of financial assets to total assets). Financial assets encompass a range of items, including money market funds, commercial financial assets, marketable securities, held-to-maturity investments, derivative financial instruments, net loans and advances, long-term equity investments, real estate, and dividend and interest receivable. Findings The findings indicated that when economic policy uncertainty remained within an optimal range, an increase in such uncertainty could stimulate business investment in tangible assets rather than in financial assets. Conversely, extremely high levels of economic policy uncertainty might lead companies to increase their investments in financial assets. However, excessive investment in financial assets could negatively impact the real economy. Therefore, it was essential for the government to enhance the transparency of its macroeconomic policies and adopt flexible transitions in economic policy to mitigate the adverse effects of excessive financialization, particularly during periods of economic downturn. Simultaneously, companies had to critically assess the risks and opportunities arising from economic policy uncertainty and make informed investment decisions. Furthermore, the findings revealed that financial constraints influenced the relationship between economic policy uncertainty and corporate financialization. This suggested that different types of firms exhibited varying degrees of sensitivity and responsiveness to economic policies under diverse economic conditions. Consequently, these factors had to be taken into account when evaluating the impact of economic policy uncertainty. Firms facing significant financial constraints might need to adjust their capital structures by reallocating financial assets to maintain normal business operations through cash flow management. In contrast, when economic policy uncertainty was high, firms with lower financial constraints might experience reduced operational risks and could leverage their financial resources to invest in financial assets for potentially higher returns. Discussion & Conclusion The findings offer valuable insights for policymakers aiming to manage corporate financial levels and mitigate the risk of financial crises. Given the significant impact of economic policy uncertainty on corporate financialization, Iranian policymakers should recognize its adverse effects on the real economy and work to reduce this uncertainty in order to foster a stable business environment for enterprises. Additionally, the government should collaborate with businesses to enhance their financialization channels. When management has access to reliable and substantial financialization services, the likelihood of excessive reliance on financialization products diminishes. Finally, greater attention should be directed toward small enterprises, non-governmental organizations, poorly governed companies, and those experiencing slower growth. These entities often face greater financial constraints and are less equipped to navigate the financial processes necessary for sustainable operations.
This study aims to investigate herd behavior across nine industry groups within the Tehran Stock Exchange from 2015 to 2023. Utilizing the methodology proposed by Chang et al. (2000), we analyze daily and weekly fluctuations in the market to assess herd behavior during periods of market volatility. The findings reveal significant herd behavior in nearly all industry groups, suggesting a pervasive phenomenon across the overall market. Notably, herd behavior is predominantly observed during upward trends within both daily and weekly time frames, potentially contributing to stock market surges, such as the notable rise from 2018 to mid-2019, which led to substantial price bubbles. Furthermore, by segmenting the study period into downward and upward phases, we explore the symmetry of herd behavior, revealing asymmetries in many industry groups.Keywords: Behavioral Finance, Herd Behavior, Total Index, Equal-Weighted Index, Tehran Stock Exchange IntroductionBarber and Odean (1999) introduced behavioral finance as a framework that elucidates irrational investor behaviors and enhances our understanding of inefficiencies in financial markets. A key concept within this domain is herd behavior, which emerged in the literature during the early 1990s. For instance, Banerjee (1992) explored herd behavior in abstract settings, illustrating how once a certain number of brokers favored a particular option, subsequent brokers tended to imitate this choice while disregarding their own information. The investigation of herd behavior in the Tehran Stock Exchange (TSE) has become increasingly relevant in recent years due to the market's experience with various currency crises and multiple upward and downward trends. Over the past decade, the TSE has undergone significant growth, marked by a dramatic increase in the number of active trading codes and a sharp rise in their trading values, underscoring the necessity of examining herd behavior in this context. Consequently, this study aims to investigate herd behavior within the TSE across nine distinct industry groups. Analyzing herd behavior by industry is particularly important given the high correlation among firms within each group and the simultaneous influence of macroeconomic news on companies operating in the same sector. Materials & MethodsChristie and Huang (1995) were pioneers in the empirical study of herd behavior in financial markets, employing an econometric approach to illustrate that the decision-making processes of market participants are influenced by prevailing market conditions. Building on their work, Chang et al. (2000) introduced a new model for identifying herd behavior, positing that investors often lose confidence during stressful periods, such as market bubbles or downturns, which leads them to follow prevailing market trends. The methodology for detecting herd behavior as proposed by Chang et al. (2000) is encapsulated in Equation 3. 𝐶𝑆A𝐷𝑡 = 𝛾0 + 𝛾1 |𝑅𝑚,t | + 𝛾2 R 2𝑚,t + 𝜀𝑡 (1)In Equation 3, CSADt represents the cross-sectional absolute deviation, which is utilized to measure the dispersion of stock returns relative to the average market return. The calculation of the cross-sectional absolute deviation is detailed in Equation 4. (2) In whick, Rm,t and Ri,t denote the average market return and the return of stock i at time t, respectively. N refers to the number of companies within the relevant industry selected for estimating herd behavior in that sector. The variable t represents the time frame used for calculating returns and absolute deviations; this study employs both daily and weekly intervals to analyze herd behavior. Additionally, it is important to note that the total index return and the equal-weighted index return are utilized as measures of the average market return in the current analysis. FindingsThe results of the analysis of herd behavior within industry groups, utilizing both the total index and the equal-weighted index, are summarized in Table 1. Table (1): Summary of results related to herd behavior in industry groupsEqual-weighted index Industry GroupTotal daily periodTotal weekly periodDaily upwardWeekly upwardDaily downwardWeekly downward Pharmaceutical companies●●○●●○ Basic Metals companies●○○○●○ Sugar production companies●○○○●○ Food Production companies●○○○●○ Automobile manufacturing companies●●●●●○ Oil refining companies○○●●●○ Investment companies●○○●●○ Chemical companies●●○●●○ Cement companies●●○●●○ Percentage of herd behavior presence894422671000 Total index Industry groupTotal daily periodTotal weekly periodDaily upwardWeekly upwardDaily downwardWeekly downward Pharmaceutical companies●●●●●○ Basic Metals companies●○●○●○ Sugar production companies●○●●●○ Food Production companies●●●●●○ Automobile manufacturing companies●●●●●○ Oil refining companies●○●○●● Investment companies●○●●●○ Chemical companies●●●●●○ Cement companies●●●●●○ Percentage of herd behavior presence100561007810011 Indicates the presence of herd behavior and ○ indicates the absence of herd behavior.According to the results reported in Table 1, evidence of herd behavior is observed in nearly all studied groups at least during one period, suggesting a prevalent presence of herd behavior across the Tehran Stock Exchange. When using the equal-weighted index as the average market return, the most significant herd behavior was identified in the group of automobile and parts manufacturers, followed by pharmaceuticals, cement, and chemical companies. Notably, no herd behavior was detected in any group during the downward trend of the weekly period. In contrast, when employing the total index as the average market return, the groups that exhibited the most herd behavior included pharmaceuticals, food, automobile and parts manufacturing, chemicals, and cement. Additionally, herd behavior was only observed in the oil refining group during the downward trend of the weekly period. Overall, both indices indicated that herd behavior was more pronounced in the daily time frame compared to the weekly time frame. The separation of the entire period into bullish and bearish phases revealed that when using the overall index as the average market return, herd behavior was more prevalent in both bullish and bearish daily time frames, indicating symmetry in the occurrence of herd behavior during this interval. In contrast, employing the equal-weighted index did not yield this symmetry; instead, more intense herd behavior was noted during the bearish daily time frame. Furthermore, across both indices, a greater degree of herd behavior was observed in the bullish phase compared to the bearish phase within the weekly time frame. Discussion & conclusionThe results demonstrate significant herd behavior across nearly all industry groups, indicating its pervasiveness within the overall market. The findings reveal that herd behavior is most pronounced during upward trends in both daily and weekly time frames, which may contribute to stock market increases, such as the notable rise from 2018 to mid-2019 that led to substantial price bubbles. Additionally, the analysis identifies the highest levels of herd behavior within the automobile manufacturing sector, while the oil refining sector exhibits the lowest. The results further indicate that herd behavior is more prevalent in the daily time frame compared to the weekly time frame, aligning with the emotional contagion aspect of this phenomenon. By partitioning the entire study period into upward and downward phases, the investigation of symmetry in herd behavior reveals asymmetry in many groups. This asymmetry may be attributed to market one-sidedness or cognitive factors such as loss aversion, as suggested by Kahneman and Tversky's Prospect Theory.
This study investigates and predicts the likelihood of operational risk occurrence in the banking industry using machine learning algorithms. The primary objective is to analyze operational risk data and evaluate the performance of various machine learning models to develop effective tools for enhancing risk management and minimizing financial losses in banks and financial institutions. Operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and k-Nearest Neighbors (KNN). Model performance was assessed using evaluation metrics such as accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC) to determine the most effective model for risk prediction. The findings indicate that the RF and SVM algorithms outperform other models in predicting operational risk across all scenarios. Furthermore, the results demonstrate the strong predictive capability of machine learning algorithms in assessing operational risk, highlighting their potential as valuable decision-making tools for risk management in the banking sector.Keywords: Risk Prediction, Operational Risk, Risk Management, Machine Learning IntroductionOperational risk is defined as the risk arising from external factors or failures in internal controls or information systems, which may lead to both anticipated and unexpected losses (Crouchy et al., 1998). Lopez (2002) characterizes it as any unquantifiable risk that a bank may encounter. According to the Basel II Agreement, operational risk refers to the probability of loss resulting from deficiencies, breakdowns, or inefficiencies in human resources, processes, technologies, infrastructure, or internal and external events (Pena et al., 2018).To estimate the capital required to cover operational risk, the Basel framework introduces three approaches: the Basic Indicator Approach (BIA), the Standardized Approach (SA), and the Advanced Measurement Approach (AMA) (Mora Valencia, 2010; Mora Valencia et al., 2017). The BIA and SA estimate capital requirements based on annual gross income, with the key distinction being that the SA categorizes a bank’s activities into eight business lines. Under the BIA, an alpha coefficient (α) of 15% is applied, whereas in the SA, each business line has a specific beta coefficient (β) ranging between 12% and 18%. The AMA employs both quantitative and qualitative methods for operational risk modeling, leveraging databases to collect statistical data and utilizing the loss distribution approach (LDA) to model frequency and severity distributions. Capital coverage is then determined based on the cumulative distribution of these variables. Since the LDA is data-driven, the Basel framework (BCBS, 2004) emphasizes the necessity of a robust database for collecting operational risk data. Four key databases are required: internal loss event data, external loss event data, scenario-based analysis data, and a database of business environment and internal control factors.Compared to other banking risks, such as credit and market risks, measuring, monitoring, and managing operational risk is considerably more complex. This risk has gained increasing attention in recent years, as large operational losses have led to the liquidation of financial institutions (Abdymomunov et al., 2020; Afonso et al., 2019). Crisanto and Perino (2017) identify cyber threats and cyber fraud as critical factors influencing operational risk capital estimation. These risks have intensified with the growth of electronic banking services and include illegal access, system disruptions, and the misuse or theft of digital assets for financial gain (BCBS, 2016; Drew & Farrell, 2018). To quantify potential losses in electronic banking transactions, Bouveret (2018) proposed a Bayesian Network (BN) model to estimate operational risk capital requirements in financial institutions.Machine learning has emerged as one of the most promising yet challenging approaches in modern finance (Tsai & Wu, 2008). These methods have transformed the financial industry, with deep learning (DL) being extensively studied and applied due to its adaptability and predictive capabilities (Ivanov, 2019). Pena et al. (2021) employed a fuzzy convolutional deep learning model to estimate the maximum operational risk value at a 99.9% confidence level. Similarly, Zhou et al. (2020) utilized semi-supervised machine learning algorithms to classify operational risks based on financial news, analyzing 5,843 documents from financial articles and newspapers in the Asia-Pacific region between February and March 2019. Their model demonstrated the capability to predict various types of risks in the banking industry. In another study, Akbari and Yazdanian (2023) applied machine learning algorithms to determine optimal thresholds for operational loss severity data, classifying the data and estimating the capital required to cover operational risk by integrating severity and frequency distribution functions with Monte Carlo simulation. Method and DataIn this study, operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including RF, DT, SVM, LR, NB, and KNN. The models' performance was assessed using evaluation metrics such as accuracy, precision, recall, F1-score, and AUC to identify the most effective model for predicting the likelihood of risk occurrence. FindingsThe results indicate that the RF and SVM algorithms exhibit strong performance in predicting operational risk across all scenarios. Specifically, the RF algorithm achieved an accuracy of 0.9690, while the SVM algorithm attained an accuracy of 0.9587 in State 1, making them the most effective models in this setting. Both algorithms demonstrated comparable performance across other modes. Conclusion and DiscussionThis study analyzes and predicts operational risk occurrence in the banking industry using machine learning algorithms. The findings indicate that various algorithms, particularly RF and SVM, demonstrate strong predictive performance. These results have the potential to transform operational risk management in banks, leading to significant reductions in associated costs and losses.A key insight from this study is that leveraging large and diverse datasets can substantially enhance prediction accuracy. Machine learning models can process complex datasets, identify hidden patterns, and facilitate early risk detection, enabling banks to implement preventive measures before risks materialize. Moreover, integrating machine learning into risk management enhances decision-making by providing precise, data-driven predictions, allowing for more effective strategies and efficient resource allocation.Future research could incorporate additional data, such as historical records, economic indicators, and internal process information, to further improve prediction accuracy. With advancements in technology, more sophisticated techniques—such as reinforcement learning methods (e.g., DQN, Q-Learning, DDPG, and Meta-Learning)—could enhance the accuracy and efficiency of operational risk prediction models.
This study aimed to investigate how companies adjust the speed at which they move toward their target financial leverage in response to stock price crash risk. Additionally, it examined the role of accounting conservatism in enhancing the speed of leverage adjustment under these risk conditions. The sample comprised data from 101 companies listed on the Tehran Stock Exchange (TSE) over a 10-year period (2013 to 2022) with analyses conducted using EViews and Stata software to test the hypotheses. The findings indicated that stock price crash risk had a negative and significant effect on the speed of leverage adjustment. Specifically, as the risk of a stock price crash increased, the companies tended to slow their adjustment toward target leverage. Conversely, the analysis of the second hypothesis revealed that accounting conservatism did not significantly moderate the relationship between stock price crash risk and the speed of leverage adjustment. However, accounting conservatism had a positive and significant direct effect on the speed of leverage adjustment.Keywords: Stock Price Crash Risk, Speed of Leverage Adjustment, Conservative Accounting.JLE: D25, D53, M41 IntroductionDebt is a critical financing instrument for companies and firms typically strive to adjust their leverage to an optimal level. According to trade-off theory, maintaining leverage at this optimal level maximizes a company's market value. One significant determinant of optimal leverage is information asymmetry. Companies operating in environments with high information asymmetry face elevated financing costs, which subsequently reduce both the frequency and speed of leverage adjustments. Dynamic trade-off theory further posits that companies seek to optimize their capital structures, with transaction costs playing a pivotal role in determining the speed of adjustment. Additionally, a positive relationship exists between stock price crash risk and information asymmetry, indicating that such risk can hinder the speed at which companies adjust their leverage. In situations characterized by information asymmetry, accounting conservatism can help alleviate these challenges and mitigate the likelihood of stock price crashes. Conservative accounting practices tend to communicate negative information to the market more promptly than positive news, thereby reducing the risk of misleading investors. Consequently, firms facing stock price crash risk may struggle to adjust their leverage swiftly due to the associated high financing costs. This study aimed to investigate whether accounting conservatism could enhance the speed of leverage adjustment in the context of stock price crash risk. Materials & MethodsThis study employed both experimental and statistical methods to test the hypotheses. By utilizing post-event observations, the study minimized the potential for variable manipulation. The findings were relevant not only to the academic community, but also to regulators, business practitioners, and stakeholders. Data were obtained from multiple sources: the Tehran Stock Exchange (TSE) database for stock returns, Rahvard software for financial statement information, and the IRI Central Bank's website for annual inflation data. The research period spanned a decade, from 2012 to 2021, encompassing 101 companies and generating a total of 1,010 firm-year observations. Data analysis was conducted using EViews and Stata software. FindingsThe results indicated that stock price crash risk had a negative and significant effect on the speed of leverage adjustment. Specifically, an increase in stock price crash risk hampered a company’s progress toward its target leverage, thereby inhibiting swift adjustments. This finding aligned with dynamic trade-off theory, which asserts that firms must consider transaction costs and suboptimal leverage ratios when making adjustments to their leverage (Fischer et al., 1989; Goldstein et al., 2001; Strebulaev, 2007). When the costs associated with rapid adjustments exceeded transaction costs, the firms might postpone such adjustments until the benefits justify the costs of recapitalization. Moreover, the companies facing substantial stock price crash risk often experienced increased information asymmetry between management and external investors, resulting in elevated financing costs. As financing costs rose, the speed at which companies adjusted their leverage toward an optimal level diminished (Kim & Zhang, 2016). Therefore, it was reasonable to conclude that high stock price crash risk impeded financial leverage adjustment. Interestingly, the findings also revealed that while accounting conservatism did not significantly moderate the relationship between stock price crash risk and leverage adjustment speed, it did have a positive and significant impact on leverage adjustment as an independent variable. Companies that employed conservative accounting practices might be more effective in adjusting their financial leverage in response to stock market risks. The literature on accounting conservatism supports this research's hypothesis. Prior studies suggested that accounting conservatism mitigates the accumulation and concealment of negative information, thereby reducing the likelihood of a sudden release of bad news into the market. As conservatism increases, the probability of hidden bad news decreases, which in turn diminishes the risk of stock price crashes and facilitates leverage adjustment. Additionally, conservatism limits managerial incentives to delay the disclosure of negative information, thereby expediting the release of positive news through voluntary disclosures. This not only reduces stock price crash risk, but also lessens the potential for price bubbles, which are a significant source of crash risk (Kim & Zhang, 2016). However, the findings indicated that accounting conservatism did not act as a moderator between stock price crash risk and leverage adjustment speed. Discussion & ConclusionAccounting conservatism involves a cautious approach to financial reporting, where losses and expenses are recorded more promptly, while revenues and gains are recognized at a later date. While this approach can enhance transparency and reduce reporting risks, it may also diminish managers' willingness to undertake bold financial decisions. In situations that require leverage adjustments, managers might hesitate to adopt risky or innovative strategies due to concerns about negative outcomes and increased risk (LaFond & Watts, 2008). Consequently, although accounting conservatism promotes transparency and mitigates the accumulation of negative information, it may not have an immediate and direct impact on the speed of leverage adjustment as it potentially reduces managerial incentives to take risks. Additionally, there may be a timing mismatch between conservative financial reporting and managerial decisions regarding leverage adjustments. The timing of financial reports may not align with decisions about leverage adjustments, thereby weakening the effect of conservatism on the speed of adjustment (Dechow & Sloan, 1991; Ball et al., 2000). In conclusion, stock price crash risk presents significant financial and economic challenges for companies that extend beyond the capacity of accounting conservatism to fully address. In scenarios of severe financial crises, conservatism alone may not adequately counterbalance the negative effects of crash risk. As Watts (2003) notes, the limitations of accounting conservatism become particularly evident in such extreme conditions and it may fall short of fully mitigating all associated challenges and risks.
The purpose of this study is to investigate the relationship between financialization and financing maturity mismatch concerning the debt issues faced by Iranian firms, aiming to provide insights for managers and policymakers. The statistical sample consists of 143 active firms listed on the Tehran Stock Exchange over a ten-year period, from 2013 to 2022. The regression method was employed to estimate the models and test the research hypotheses. The findings indicate no significant relationship between financialization and the long-term use of short-term debt. However, distinct results emerged when firms were categorized into large and small entities. In large firms, financialization appears to positively affect the long-term use of short-term debt, in contrast to smaller firms. Additionally, financial constraints do not significantly influence the propensity of firms to utilize short-term debt and do not incentivize increased willingness among these firms to rely on short-term financing in the long term. Furthermore, financialization leads to a decrease in investment in productive assets, with firms that are more inclined toward financialization utilizing less internal financing. While previous studies on maturity mismatch have highlighted factors such as external regulations, the macroeconomic environment, and internal corporate governance, this study addresses a gap in the research concerning the discrepancy between corporate debt and investment horizons. It provides empirical evidence at the firm level and investigates the underlying mechanisms through which financialization influences the long-term use of short-term debt.Keywords: Capital Structure, Debt Maturity Structure, Financialization, Tangible Long-Term InvestmentsJEL Classification: G11، G31، P45 IntroductionAccording to the principle of matching investment and financing maturities, the maturity of a firm's debt should correspond to the maturity of its assets (Chen et al., 2023). The long-term use of short-term debt (LUSD) is a critical aspect of debt maturity mismatch, wherein short-term debt is allocated to support long-term investments. Analyzing the factors influencing LUSD is essential for gaining a comprehensive understanding of corporate financing decisions. The phenomenon of corporate financialization is prevalent globally and is closely linked to corporate investment and financing practices (Cao et al., 2022). Financialization may seem to reduce investment in fixed assets, potentially leading to diminished firm performance and economic recession (Tori & Onaran, 2018). Consequently, firms may encounter financing constraints, making it more challenging to secure long-term loans and resulting in an increased reliance on LUSD. Conversely, financialization may also be driven by hedging strategies and the pursuit of higher returns, which can alleviate financing constraints and improve firm performance, thereby reducing reliance on LUSD to some extent (Gong et al., 2023). Thus, the relationship between corporate financialization and LUSD remains ambiguous and warrants further investigation. This study aims to explore the relationship between financialization and financing maturity mismatch concerning the prevailing debt issues among Iranian firms, with the intent of providing insights for managers and policymakers. The findings are expected to offer valuable guidance for aligning fiscal policies, financing strategies, and investment initiatives. Materials & MethodsThe dependent variable of this study is the long-term use of short-term debt (LUSD), measured as the difference between the ratio of short-term liabilities to total liabilities and the ratio of short-term assets to total assets. Financialization serves as the independent variable, defined as the ratio of total financial assets to total assets. Financial assets encompass short-term and long-term investments, non-trade receivables, prepayments, and investments in real estate. Additionally, financial constraints are incorporated as an interactive variable, represented by a dummy variable. Firms with a financial cost-to-total debt ratio exceeding the median are classified as facing resource acquisition restrictions and assigned a value of one; otherwise, they are assigned a value of zero. The sample includes active firms listed on the Tehran Stock Exchange (TSE). The sample includes 143 firms over a ten-year period from 2013 to 2022. To explore the relationships among the variables, multiple linear regression analysis was conducted. The data were collected from firms' financial reports, the Codal system, and other reliable financial sources. Findings The findings indicate that, contrary to existing literature, there is no significant relationship between financialization and the long-term use of short-term debt (LUSD). However, when firms are categorized into large and small entities, distinct results emerge. In large firms, financialization has a positive effect on LUSD, whereas in small firms, financialization exerts a negative impact on LUSD. Additionally, the results suggest that financial constraints do not significantly influence the use of short-term debt and do not serve as an incentive for firms to increase their reliance on short-term financing in the long term. Furthermore, financialization is associated with a reduction in investment in productive assets. The findings also indicate that financialization diminishes firms' willingness to obtain internal financing. This suggests that firms inclined toward financialization are less likely to seek internal funds, thereby increasing their dependence on external borrowing. Discussion & Conclusion Given the high level of financialization in the sample firms, it is suggested that there should be an optimal degree of financialization, with careful consideration of the economic consequences of excessive financialization. In response to the current situation, it is recommended that enterprises focus on their core business, clarify their development priorities, and engage in financialization activities strategically at the micro level. Additionally, participation in corporate governance is essential. At the macro level, while guiding the development of the financial industry, it is also necessary to stimulate innovation in the economic value of enterprises and enhance their overall strength. Given the negative impact of financialization on investment in productive assets, it is crucial to foster a greater willingness and confidence among enterprises to invest in production units and encourage investment in the real economy. Governments and local institutions should effectively support enterprises by implementing robust policies for the real industry and creating a favorable business environment. Considering the positive impact of financialization on LUSD in large firms, managers are advised to rely more on long-term financing to better manage financial risk and working capital, thereby preventing default risk. Furthermore, in light of the negative impact of financialization on domestic financing—which diminishes its benefits—it is essential to streamline both direct and indirect financing channels for companies, improve the capital market environment, and expand the routes for long-term capital supply in the market.
Theoretical and empirical evidence indicates that a more highly educated workforce can enhance the quality of accounting information, reduce information asymmetry, and improve managerial oversight, ultimately contributing to increased investment efficiency. Therefore, this study aims to investigate the mediating role of accounting information quality in the relationship between workforce education level and investment efficiency. This study is designed to support decision-making processes. Utilizing a descriptive-correlational methodology, the research analyzes the relationships among various variables. The sample comprises 141 firms listed on the Tehran Stock Exchange from 2009 to 2023. Data analysis was performed using Stata (version 17) and EViews (version 13) software. The results demonstrate that workforce education level not only has a direct effect on investment efficiency but also enhances it through improved accounting information quality. Employees with higher education are capable of providing superior information, effectively monitoring managerial activities, and mitigating misconduct. This, in turn, enhances information accuracy, financial transparency, and investment efficiency. The findings underscore the significance of workforce education in enhancing accounting information quality and investment efficiency, offering valuable insights for managers and policymakers. Keywords: Accounting Information Quality, Investment Efficiency, Workforce Education Level. JLE: D25, D53, M41 Introduction The research literature, grounded in agency theory, suggests that high-quality accounting information and disclosure can reduce information asymmetry, enhance managerial oversight, and mitigate opportunistic behavior. Empirical evidence indicates that information asymmetry negatively impacts investment efficiency. Consequently, improving the quality of accounting information is crucial for enhancing investment efficiency. In this context, recent studies emphasize that a more highly educated workforce can positively influence firms' investment efficiency by elevating the quality of accounting information. This study focuses on the role of workforce education level in improving accounting information quality and investment efficiency while also examining the mediating effect of accounting information quality. The aim of this research is to provide scientific evidence to address the existing research gap in this area and to illuminate the significance of workforce education in enhancing corporate investment efficiency. Methods & Materials The data were extracted from Rahavard Novin software, financial statements, and board activity reports pertaining to workforce education levels. Following data collection, preliminary processing was conducted using Excel, while final analysis was performed with EViews (version 13) and Stata (version 17). The sample comprises companies listed on the Tehran Stock Exchange from 2009 to 2023, selected based on specific criteria: inclusion on the stock exchange as of the fiscal year ending in March 2008, a consistent fiscal year ending in March, no changes in business activities or fiscal year during the study period, exclusion of financial intermediaries and investment companies, sustained listing until the end of 2023, and the availability of necessary data. A total of 141 firms were selected, resulting in 2,115 firm-year observations. Findings The results indicate that workforce education level has a positive and significant effect on investment efficiency. This suggests that as workforce education levels increase, investment efficiency also improves. The research literature posits that highly educated employees can enhance corporate investment efficiency through three primary mechanisms: first, by providing high-quality information that aids managers in effective decision-making, as these employees excel in collecting and analyzing valuable data; second, by effectively monitoring managerial decisions due to their access to internal information and their ability to detect misconduct, which helps prevent inaccurate financial reporting; and third, by facilitating information exchange between managers and employees through superior communication skills, enhanced understanding, and shared educational backgrounds, thereby leading to improved accuracy in information transfer and corporate decision-making. The findings from the first hypothesis are consistent with the studies by Jin et al. (2023) and Chen et al. (2023). The results of the second hypothesis further align with prior research, indicating a positive and significant effect of workforce education level on the quality of accounting information. A more educated workforce can enhance accounting information quality through various mechanisms, such as providing data to accounting systems that is less prone to errors (Andreou et al., 2017), identifying and reporting irregular or potentially fraudulent transactions (Call et al., 2016), improving the effectiveness of internal controls and reducing risks (Fu et al., 2020), and increasing transparency in financial reporting (Call et al., 2017). The findings from the second hypothesis are consistent with studies by Liu et al. (2017), Call et al. (2017), and Afieh et al. (2020). Finally, the third hypothesis, which examines the mediating role of accounting information quality in the relationship between workforce education level and investment efficiency, was not rejected. This finding demonstrates that workforce education level not only directly influences investment efficiency but also facilitates investment efficiency through the enhancement of accounting information quality. The findings from the third hypothesis are consistent with the study by Chen et al. (2023). Discussion and Conclusion The findings suggest that a more highly educated workforce can provide higher-quality information, effectively monitor managerial performance, and prevent misconduct. Educated employees, equipped with superior communication skills and a deeper understanding of shared educational backgrounds, facilitate accurate information exchange and informed decision-making. Workforce education level contributes to improved investment efficiency not only directly but also indirectly through enhanced accounting information quality. This enhancement involves reducing reporting errors, detecting and addressing irregularities, and increasing transparency in financial reporting. The study underscores the significance of workforce education in augmenting both accounting information quality and investment efficiency. Companies should prioritize employee education and training to fully leverage these potential benefits. Recruitment processes should place greater emphasis on educational qualifications and specialized skills to maximize workforce capabilities. Furthermore, investors are advised to consider workforce education levels in their evaluations, as this reflects the company's potential for achieving high investment efficiency and superior accounting information quality.
Crowdfunding campaigns on social media have emerged as a powerful tool for entrepreneurs seeking to secure capital and mobilize support for their ventures. However, the success of these campaigns is often uncertain, and a comprehensive framework to guide practitioners in creating effective campaigns is currently lacking. The primary objective of this research is to develop a framework for successful crowdfunding campaigns on social media through a systematic review of the literature. A qualitative systematic literature review methodology was employed. Initially, 1,330 studies were screened, and after multiple stages of filtering, 29 studies were selected for in-depth analysis. The findings yield a framework that encompasses the components of successful social media crowdfunding campaigns (including project goals, brand awareness, and fundraising amounts), factors influencing campaign success (such as project characteristics, entrepreneur attributes, campaign strategy, social media activity, and psychological factors), the consequences of successful campaigns (including financial, social, and network outcomes), and intervening factors (such as platform characteristics and time constraints). This framework serves as a valuable guide for entrepreneurs, platform providers, and policymakers in formulating effective strategies to enhance the success of crowdfunding initiatives.Keywords: Crowdfunding, Social Media, Crowdfunding Success, Entrepreneurship, Systematic Literature Review. IntroductionCrowdfunding has emerged as a popular and innovative method for financing projects and businesses, enabling individuals to contribute small amounts of money to transform ambitious ideas into reality. Various online platforms facilitate crowdfunding campaigns, during which projects are showcased for a specific duration, with their success or failure determined by the amount of funds raised. Social media platforms play a pivotal role in the success of these campaigns, serving as powerful tools for promoting and sharing projects and allowing individuals to reach their target audiences effectively. Despite the significance of this topic, comprehensive research on the factors influencing the success of crowdfunding campaigns on social media remains limited. The primary objective of this study is to present a comprehensive model for predicting the success of crowdfunding campaigns on social media. This study's significance lies in its potential to help individuals and organizations design and implement more successful crowdfunding campaigns by offering a robust predictive framework. Furthermore, this research contributes to advancing knowledge in the field of crowdfunding and elucidates the role of social media in this context. In this study, a thorough review of the existing literature informs the development of a comprehensive model for the success of crowdfunding campaigns on social media. This model can serve as a valuable tool for researchers, entrepreneurs, and investors engaged in this field.Materials & MethodsThis study employed a qualitative approach through a systematic literature review (SLR) guided by the established seven-step method outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins & Green, 2008). The process included: (1) formulating a focused research question; (2) determining clear inclusion criteria; (3) conducting a comprehensive search for relevant studies; (4) rigorously selecting studies based on pre-defined criteria; (5) critically appraising the methodological quality of included studies; (6) systematically extracting relevant data; and (7) synthesizing and presenting the findings. A comprehensive search strategy was implemented across a range of reputable scientific databases, including domestic databases such as Noormags, SID, ensani.ir, and Magiran to capture relevant research in the local context, along with international databases such as ScienceDirect, Springer, Wiley, Sage, Emerald, and Taylor & Francis to ensure a broader search. The search encompassed publications from 2000 to 2024 and resulted in an initial collection of 1,330 studies related to crowdfunding campaigns on social media. Following a rigorous screening process based on pre-defined selection criteria—relevance to the research topic, methodological quality, and clarity of findings—the final sample for analysis comprised 29 studies. FindingsThis research aimed to develop a comprehensive framework for the success of crowdfunding campaigns on social media. Through a systematic review of the literature, it was determined that multiple factors influence the success of a crowdfunding campaign. These factors include project characteristics, the entrepreneur's attributes and abilities, campaign strategy, social media activity and interactions, social network indicators, psychological factors, platform features, and moderating variables such as time constraints and platform characteristics. Campaign success can be measured by various dimensions, including the total amount of funds raised, increased brand awareness, and the achievement of specific project goals. The research delved into the influence of various factors on campaign success. These factors encompass inherent project characteristics, the entrepreneur's personal attributes and abilities, the strategies implemented during the campaign, interactions occurring on social media, and psychological factors that impact investor decision-making. Moreover, this research explored the potential consequences of successful crowdfunding campaigns. These consequences include financial benefits, strengthened social relationships, and expanded professional networks. In conclusion, the findings of this research indicate that the success of crowdfunding campaigns on social media is a complex interplay of various factors. By gaining a deeper understanding of these factors, a robust framework can be developed to design and implement more effective crowdfunding campaigns. Discussion of Results & ConclusionThis study aimed to develop a comprehensive framework for the success of crowdfunding campaigns on social media. Through a systematic review of the literature, it was found that multiple factors influence the success of these campaigns. Key factors include project characteristics, the entrepreneur's attributes and abilities, campaign strategy, social media activity and interactions, social network indicators, psychological factors, platform features, and moderating variables such as time constraints. Campaign success can be measured across various dimensions, including the total amount of funds raised, increased brand awareness, and the achievement of specific project goals. The study examined the impact of these factors on campaign success, highlighting the importance of inherent project characteristics, the entrepreneur's personal attributes, the strategies employed during the campaign, social media interactions, and psychological factors that affect investor decision-making. Additionally, the study explored the potential consequences of successful crowdfunding campaigns, which include financial benefits, strengthened social relationships, and expanded professional networks. In conclusion, the findings indicate that the success of crowdfunding campaigns on social media is a complex interplay of various factors. By gaining a deeper understanding of these elements, a robust framework can be developed to design and implement more effective crowdfunding campaigns.
