stock exchange
Leila Farvizi; Sakineh Sojoodi; Hossein Asgharpour; Jafar Haghighat
Abstract
Numerous studies have investigated the relationship between systematic risk and a wide range of accounting and financial variables. However, most empirical studies have adopted the classical regression method, which entails limitations such as a restricted number of variables to preserve degrees of freedom. ...
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Numerous studies have investigated the relationship between systematic risk and a wide range of accounting and financial variables. However, most empirical studies have adopted the classical regression method, which entails limitations such as a restricted number of variables to preserve degrees of freedom. To overcome this constraint, the present study employs the Bayesian Model Averaging (BMA) method. Using data from 55 companies listed on the Tehran Stock Exchange between 2010 and 2023, this study examines the influence of 58 different financial and accounting variables on the systematic risk of these companies. The research aims to identify the key variables that significantly contribute to systematic risk. The findings reveal that among the examined variables, company size has the strongest impact on systematic risk, with a positive coefficient. In second and third place, asset turnover and operational efficiency demonstrate significant effects, with the former exhibiting a positive coefficient and the latter a negative coefficient. The fourth influential variable is the ratio of long-term debt-to-equity, showing a positive coefficient. Lastly, the ratio of a company's market value to the book value of its total assets is identified as the fifth influential variable, exerting a negative impact on systematic risk. IntroductionUnderstanding the drivers of systematic risk is crucial for investors seeking to optimize their portfolios and for companies aiming to develop robust risk management strategies. While many studies have explored the relationship between systematic risk and various accounting and financial variables, the majority have used classical regression methods, which tend to focus on a limited number of factors. This limitation often overlooks the complex interplay among variables that could better explain systematic risk. Given the growing need for more accurate models in the face of financial market volatility, this study adopts the Bayesian Model Averaging (BMA) approach to assess the impact of a wider range of accounting and financial variables on systematic risk. The research seeks to answer the following questions:Research Question(s)- Which accounting and financial variables most significantly influence the systematic risk of companies listed on the Tehran Stock Exchange?-Do the selected variables have a positive or negative impact on systematic risk, and how do these effects vary across different industries and financial contexts?2- Literature ReviewSystematic risk, commonly measured by the beta coefficient, represents the portion of a company’s risk that cannot be diversified away. Previous studies have highlighted several accounting and financial factors, including company size, financial leverage, operational efficiency, and asset turnover, as important determinants of systematic risk (Figure 1). However, the results across studies are mixed, and traditional models often fail to account for the complex interactions among variables. Additionally, several studies have noted that the method of variable selection and estimation can significantly influence the conclusions drawn about risk determinants. The literature suggests that large firms tend to have higher systematic risk due to greater exposure to market and economic cycles, while smaller firms may experience lower risk due to reduced exposure to such fluctuations. Other studies have explored the roles of profitability, debt ratios, liquidity, and asset management in determining market risk, but there is no consensus on which variables are most influential. Figure1- Fundamental Factors Affecting Systematic RiskSource: Brimble & Hodgson (2007) 3- MethodologyThis study employs the BMA technique to assess the impact of 58 potential accounting and financial variables on systematic risk. The BMA approach is particularly well-suited to this context because it enables the simultaneous consideration of multiple models, allowing for a more comprehensive understanding of the relationships between variables and risk. The study uses data from 55 companies listed on the Tehran Stock Exchange, covering the period from 2010 to 2023. The sample includes companies from a range of sectors, ensuring that the findings are not limited to any one industry. Data were collected from financial statements and reports available on the official website of the Tehran Stock Exchange (TSETMC), and the BMA method was implemented using Stata 18 software. The estimation process includes backward sampling, in which weak models are sequentially excluded and the best models are selected based on their posterior probability of explaining the data.4- ResultsThe results of the BMA analysis indicate that several variables have a significant impact on systematic riskCompany Size: Company size has the strongest effect on systematic risk, with a positive coefficient, indicating that larger companies generally face higher systematic risk.Asset Turnover: The asset turnover ratio, which measures how efficiently a company uses its assets to generate revenue, also has a positive effect on systematic risk.Operational Efficiency: Companies with higher operational efficiency exhibit lower systematic risk, as indicated by the negative coefficient for operational efficiency.Long-Term Debt-to-Equity Ratio: A positive relationship is found between the long-term debt-to-equity ratio and systematic risk, suggesting that companies with higher leverage tend to experience greater exposure to market risk.Market Value to Book Value Ratio: This ratio has a negative effect on systematic risk, indicating that companies with higher market valuations relative to their book values are less sensitive to market fluctuations.These variables were identified as the most significant based on their posterior inclusion probabilities (PIP), with company size having the highest PIP of 0.8143, indicating it is the most important determinant of systematic risk.5- DiscussionThe findings suggest that company size plays a pivotal role in determining systematic risk. Larger companies tend to be more exposed to broader economic fluctuations and market cycles, which can lead to higher systematic risk. Asset turnover, though generally considered a measure of operational efficiency, also contributes positively to risk, potentially due to the increased exposure of firms with higher asset turnover to volatile markets. Operational efficiency, on the other hand, shows a negative relationship with systematic risk, supporting the notion that companies with better control over their operations are more resilient to market shocks. This finding is consistent with the literature suggesting that operational efficiency can mitigate the impact of external risks. Similarly, the positive relationship between the long-term debt-to-equity ratio and systematic risk aligns with prior studies that highlight the role of financial leverage in amplifying market risk. Finally, the negative relationship with the market value to book value ratio indicates that investors view companies with higher market valuations as more stable, potentially because these companies are perceived as less vulnerable to market downturns.6- ConclusionThis study contributes to the understanding of the determinants of systematic risk by employing the BMA approach, which overcomes limitations inherent in traditional regression models. The results highlight that company size, asset turnover, operational efficiency, the long-term debt-to-equity ratio, and the market value to book value ratio are the key factors influencing systematic risk. These findings have practical implications for investors and corporate managers seeking to mitigate exposure to market risk. Companies, especially larger ones, can benefit from enhancing operational efficiency and optimizing their financial structures to reduce systematic risk. Future research could explore the interaction between these variables across different sectors and market conditions, and further refine models by incorporating additional macroeconomic factors.
Capital Structure
Sarah Mohsin; Narges Hamidian; Seyed Abbas Hashemi
Abstract
Stock price crash risk, defined as an adverse event, is a pervasive phenomenon at the market level. This implies that theStock price crash risk, defined as an adverse event, is a pervasive phenomenon at the market level. This implies that the decline in stock prices is not limited to a specific stock ...
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Stock price crash risk, defined as an adverse event, is a pervasive phenomenon at the market level. This implies that theStock price crash risk, defined as an adverse event, is a pervasive phenomenon at the market level. This implies that the decline in stock prices is not limited to a specific stock but extends across the entire market. Stock price crashes result in significant losses for shareholders and investors, as well as a decline in the overall capital market. Hence, understanding the factors influencing this phenomenon is of critical importance. The present study aims to investigate the impact of industry operating cash flow volatility on future stock price crash risk, considering the roles of economic policy uncertainty and conditional conservatism in companies listed on the Tehran Stock Exchange. A sample of 136 companies was selected using a screening method over the period from 2012 to 2022. To analyze the data and test the hypotheses, regression analysis and panel data techniques were employed. The findings indicate that industry operating cash flow volatility has a positive and significant effect on future stock price crash risk. Furthermore, economic policy uncertainty amplifies the positive effect of industry operating cash flow volatility on stock price crash risk. Conversely, conditional conservatism in accounting mitigates the positive relationship between operating cash flow volatility and future stock price crash risk. IntroductionThe expansion of the capital market is a cornerstone of economic growth and development for any country. A critical driver in advancing this sector is fostering active investor participation. To this end, ensuring transparency and providing access to relevant information for evaluating optimal investment opportunities, while considering the risk-return profile of various stocks, are essential for capital market participants. Among the risks faced by the capital market, stock price crash risk stands out as a significant concern. This risk, defined as a sharp and widespread decline in stock prices across the market, transcends individual stocks and affects the market as a whole. The implications of such crashes are profound, leading to substantial losses for shareholders and investors and potentially undermining the overall stability of the capital market. Consequently, identifying and understanding the factors that contribute to this phenomenon is of paramount importance. This study seeks to examine the effect of industry operating cash flow volatility on the future risk of stock price crashes. Furthermore, it incorporates the moderating roles of economic policy uncertainty and conditional conservatism in companies listed on the Tehran Stock Exchange. Literature ReviewThe existing literature suggests that stock price crashes result from efforts to conceal negative information within companies. This conclusion is based on the principal-agent theory by Jine and Myers (2006), which posits that management, having control over the flow of information, is motivated to withhold information, often negative, for various reasons over the long term.One of the factors contributing to stock price crash risk is the volatility of operating cash flows (Wang et al., 2022). Stable operating cash flows are a critical component of a company’s healthy and sustainable operations (Sun and Ding, 2020). Companies experiencing high volatility in operating cash flows typically have fewer cash reserves available for operational needs and are more reliant on external financing. For such companies, financing costs are higher, which, in turn, reduces their overall value (Chen and Huberman, 2014). It is essential to consider that external factors, such as macroeconomic and industry-specific conditions, significantly influence corporate decision-making. Management tends to believe that an increase in industry-level operating cash flow volatility exposes the company to a more uncertain external environment. As this volatility rises, capital market participants pay closer attention to the market, leading to a greater impact of negative information disclosures on the company’s future operations and financing decisions. Consequently, management becomes more inclined to conceal negative information, thereby increasing the risk of future stock price crashes.Moreover, heightened economic policy uncertainty exacerbates corporate policy risks, further incentivizing management to hide adverse news and information, which, in turn, increases the risk of stock price crashes (Luo & Zhang, 2020). Additionally, conditional conservatism practices counteract managerial tendencies and motivations to conceal negative information, thereby reducing the likelihood of stock price crashes and mitigating investment risks in equities (Kim & Zhang, 2016). Conditional conservatism is expected to prevent the accumulation of bad news within the company, thus reducing the sudden release of substantial negative information into the market (Pourheidari et al., 2018). Consequently, higher levels of conditional conservatism are associated with a lower accumulation and concealment of bad news, ultimately reducing the risk of stock price crashes (Antonakakis et al., 2013). Based on the above, the research hypotheses are as follows:Hypothesis 1: Industry operating cash flow volatility has a positive effect on future stock price crash risk.Hypothesis 2: Economic policy uncertainty exacerbates the positive effect of industry operating cash flow volatility on future stock price crash risk.Hypothesis 3: Conditional conservatism in accounting weakens the positive effect of industry operating cash flow volatility on future stock price crash risk. MethodologyThis study is categorized as applied research, as it aims to provide practical insights and solutions that can be directly implemented in real-world contexts. Methodologically, it adopts a descriptive-correlational approach, seeking to describe the characteristics of the variables under investigation and analyze the relationships among them. To achieve the research objectives, three hypotheses were formulated. These hypotheses examine the effects of industry operating cash flows volatility on future stock price crash risk, incorporating the moderating roles of economic policy uncertainty and conditional conservatism. The statistical sample consists of 136 companies listed on the Tehran Stock Exchange, observed over a ten-year period from 2012 to 2022. To analyze the data and test the hypotheses, regression analysis, panel data techniques, and Stata 15 software were utilized. ResultsThe analysis of the first hypothesis demonstrates that industry operating cash flow volatility exerts a positive and significant effect on future stock price crash risk. This finding underscores the destabilizing influence of variability in cash flows from core business operations, which can signal underlying financial instability and heighten the likelihood of abrupt and severe declines in stock prices. The results of the second hypothesis indicate that economic policy uncertainty intensifies the positive relationship between industry operating cash flow volatility and future stock price crash risk. This suggests that in an environment characterized by heightened economic policy uncertainty, the risks associated with cash flow variability are magnified. Unpredictable policy conditions can create additional pressure on management to obscure negative information in an effort to sustain investor confidence, further exacerbating the risk of stock price crashes. Finally, the findings related to the third hypothesis reveal that conditional conservatism in accounting mitigates the positive effect of industry operating cash flow volatility on future stock price crash risk. Conditional conservatism, characterized by the timely recognition of potential losses and liabilities over gains, serves as a counterbalance to managerial tendencies to suppress unfavorable information. By enforcing stringent accounting standards, conditional conservatism enhances the transparency and reliability of financial reporting, thereby attenuating the impact of cash flow volatility on crash risk and fostering greater stability in the capital market. ConclusionThis study examines the effect of industry operating cash flow volatility on future stock price crash risk, with a focus on the moderating roles of economic policy uncertainty and conditional conservatism in companies listed on the Tehran Stock Exchange. The findings of the first hypothesis reveal that volatility in operating cash flows signals potential risks related to a company’s future operations, investments, and financial activities. Moreover, such volatility may incentivize management to withhold adverse information, thereby increasing the likelihood of future stock price crashes. The results of the second hypothesis suggest that elevated economic policy uncertainty intensifies the risks associated with firm policies, further motivating management to conceal unfavorable information. This heightened opacity exacerbates the probability of stock price crashes, reflecting the amplified impact of cash flow volatility under uncertain policy environments. In contrast, the findings of the third hypothesis indicate that conditional conservatism in accounting practices mitigates the positive relationship between operating cash flow volatility and future stock price crash risk. By emphasizing the timely recognition of losses and liabilities over gains, conditional conservatism acts as a counterbalance to managerial tendencies to suppress negative information, thereby reducing the impact of operating cash flow volatility on crash risk. These findings align with prior research by Wang et al. (2022), Lu Zhang (2020), and Kim and Zhang (2016), further validating the theoretical and empirical linkages among operating cash flow volatility, policy uncertainty, and conditional conservatism in mitigating stock price crash risk.
stock exchange
Behrooz Badpa; Sohrab Osta; Fatemeh Darvish-Hoseini
Abstract
Working capital management is crucial for business growth and survival as it maximizes enterprise value and shareholder wealth, thereby maintaining competitive conditions and optimal performance. This study identified and explained accounting variables determining operational efficiency (OE) of the companies ...
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Working capital management is crucial for business growth and survival as it maximizes enterprise value and shareholder wealth, thereby maintaining competitive conditions and optimal performance. This study identified and explained accounting variables determining operational efficiency (OE) of the companies listed on the Tehran Stock Exchange (TSE), Iran, in light of working capital items. The statistical population consisted of all companies, and the samples were 112 cases listed during 2016-2020. Utilizing an applied, descriptive-correlational research design, the relationship between the variables was then established. The dependent variable was OE, evaluated using data envelopment analysis (DEA); and the independent ones were working capital items and dividend growth rate. To investigate the effect of the independent variables on the dependent one, eight hypotheses were formulated, and multivariate linear regression with panel data in a fixed-effects model was implemented. Testing the hypotheses at a 95% confidence interval demonstrated that average period of collection of claims, average debt repayment period, dividend growth ratio, cash holding level, and liquidity ratio have a significant positive effect on OE. Nevertheless, the cash conversion cycle, and average inventory turnover period have negative impacts. Managers are thus suggested to identify working capital items and exploit them along with short/long-term goals in companies. This is practical in evaluating financial flexibility and solvency, facilitating optimal liquidity, and increasing business profitability and performance. Furthermore, learning about such items is helpful to investors, creditors, and analysts to make optimal decisions. IntroductionWorking capital management in companies plays a key role in their growth and survival. This business process also helps increase the value of such entities and maximize their shareholder wealth, thereby maintaining competitive conditions and optimal performance. Representing the management of current resources and expenses in a company, working capital management has two components, namely, the management of current assets and liabilities, whose balance is of utmost importance. Decisions made about each one can affect the other (Jahan Khani & Talebi, 1999). On the word of Nath et al. (2010), working capital items have a critical role in the operational efficiency (OE) of a company as well as its marketing capability. In this line, Fang et al. (2008) also believe that working capital items have high liquidity, and are directly associated with the operating results and efficiency of a company, so managing cash in the short term is especially relevant for competition in markets. Therefore, the main items in working capital can significantly shape the operating results in a company, including contribution margin, market share, and OE. Against this background, the present study is to identify and explain the accounting variables determining the OE of the companies listed on the Tehran Stock Exchange (TSE), Iran, in light of the working capital items.Materials & MethodsConsidering the type of supervision and the degree of control, this study is categorized as field research, because the variables were investigated in their natural state. With regard to the data collection method, this study is placed into documentary research. Utilizing an applied, descriptive research design, the relationship between the given variables was established via a correlational study. The statistical population comprised the companies listed on the TSE, Iran, and the study samples included 112 cases listed during 2016-2020. The dependent variable was OE, evaluated using data envelopment analysis (DEA), and the independent variables were working capital items and dividend growth rate. Profitability index, company size, financial leverage, and operating cash flow (OCF) were correspondingly deemed as the control variables in the research model. To shed light on the effect of the independent variables on the dependent one, eight hypotheses were initially formulated, and then multivariate linear regression using panel data in a fixed-effects model was implemented to test them. In order to analyze the data and interpret the results, descriptive and inferential statistics were ultimately utilized.FindingsUpon presenting the descriptive statistics and checking the assumptions of the regression as well as determining themost suitable research model, the linear regression equation was estimated using the fixed-effects model, as described in table 1Discussion & ConclusionAs confirmed by the study findings, working capital items can explain the OE of the companies listed on the TSE, Iran. In this respect, the results of testing the main research hypothesis are consistent with the reports by Sun et al. (2020) and Nath et al. (2010). The outcomes of testing the secondary hypotheses also reveal a significant positive relationship between the variables of average period of collection of claims, average debt repayment period, dividend growth ratio, cash holding level, and liquidity ratio and the variable of operational efficiency. Nevertheless, there is a significant negative relationship between the variables of cash conversion cycle and average inventory turnover period and operational efficiency.Considering these results, cash holding level and liquidity ratio have a positive effect on operational efficiency, which supports the findings in Nath et al. (2010). According to Nath et al. (2010), working capital items with high liquidity help improve the OE of a company, indicating its high capability to manage cash in the short term, as a requirement for its competitive presence in markets. The study results also agree with those concluded by Afrifa et al. (2022) that holding more cash facilitates working capital efficiency. Based on the study findings, average inventory turnover period has a negative effect on OE, in harmony with the results in Deloof (2003) that high inventory level declines the profitability and performance of a company. In his opinion, managers can increase the profitability and performance of businesses by reducing inventory levels. In view of the cash conversion cycle in the given companies during the study period here, the relationship between this variable and OE is negative, which is consistent with the results in Abdulla et al. (2017) that companies with higher cash conversion cycle are more efficient in managing their working capital as compared with other entities.From this perspective, managers are suggested to identify the role of working capital items and exploit them in line with the short/long-term goals in companies. This is practical in evaluating financial flexibility and solvency, and facilitates achieving optimal liquidity, and subsequently increasing business profitability and performance. Furthermore, learning about the role of working capital items is of assistance to investors, creditors, and analysts to make optimal decisions. Furthermore, it is possible to carry out the same study in the future with respect to the size and type of industry of the companies listed on the TSE, Iran, and complete a comparative study regarding the companies operating in each industry. Besides, it is recommended to analyze the effect of various working capital strategies on economic added value in a separate study. Investigating the effect of various strategies and components of working capital on stock price and its fluctuations should also be the subject of further research.
