جستجوی مقالات مرتبط با کلیدواژه "d" در نشریات گروه "مالی"

Observers and experts of the Islamic finance industry expect Islamic financial institutions to move from offering instruments that are solely Shariah-compliant to offering instruments that are substantially Shariah-based. However, there are some questions about when the words "Shariah-compliant" and "Sharia-based" entered into the Islamic financial literature and why. What is the exact meaning of "Shariah-based"? In this article, in addition to reviewing different attitudes towards the Islamic financial system, we will describe the history of the emergence of the two mentioned terms as a manifestation of the change of discourse in the Islamic financial industry. Investigations show that in the definition of Sharia-based product, at least one of the principles of separation (emphasis on the content separation of Islamic finance from conventional finance), Authenticity and welfare have been implied. The principle of separation refers to the concepts of separate product development, separate pricing, separation of funds and sectoral separation. The principle of authenticity deals with the three concepts of historical authenticity, authentic connection to the real economy and ontological transparency. Concepts in the welfare principle include risk-sharing, protection of the poor, and welfare-oriented business operations. This change in the discourse is not a problem, but a sign of Islamic financial maturity and moving towards the goals of the Islamic economic system.

In today's world, one of the most significant concerns for economic enterprises is financing. In Muslim countries, Islamic financing is conducted through Islamic bonds known as sukuk. Sukuk come in various types, tailored to the specific needs of the issuing entity. Multiple stakeholders are involved in the issuance process of these bonds, each with a defined role to optimize the process. Consequently, each stakeholder assumes certain risks throughout this process. This study examines these risks and ranks the credit risk each stakeholder incurs during the sukuk issuance process. The research is applied in nature and utilizes a descriptive survey method for data collection, falling under the category of field studies. The analysis method for this study is TOPSIS. Data collection was conducted in two phases: initially through library research and reviewing existing documents, and subsequently through field research. The necessary raw data was gathered via questionnaires. The findings indicated that, on average, for all types of bonds, the originator bears the highest credit risk in the sukuk issuance process, while the market maker bears the lowest risk. Additionally, it was found that among all scenarios involving different types of bonds and stakeholders, in participation bonds, the originator bears the highest credit risk, whereas, in Murabaha bonds, the market maker bears the lowest risk. The results of this study can be utilized in policymaking for drafting guidelines on the issuance of Islamic financial bonds and the methods for credit evaluation, securing guarantees, and assessing the stakeholders involved in the issuance process based on the type of bond.

Extensive environmental turbulence has caused various changes in economic, social and cultural fields, and has become the driver of many changes in the context of needs and preferences of customers. As an active form of Islamic banking in Iran, Qarz-Al-Hasna Banking has faced many issues and challenges that have overshadowed its survival and competitiveness. Therefore, the aim of this research is to answer the banking needs of Qarz-Al-Hasna Bank and specifically the territory of Qarz-Al-Hasna Mehr Bank of Iran in the face of digital variables, in the form of a business model. In this regard, the method of qualitative content analysis with analogical-inductive approach has been used to analyze the data and answer the research questions. Ritter's model is the basic framework in "Designing the Business Model of Qarz-Al-Hasna Digital Bank". In the data analysis that was done using Maxqda2020 software, a total of 255 basic themes and 29 subcategories were extracted and placed under the dimensions of value proposition, customers, value demonstration and capabilities.

"Bay al-Aina" is one of the basic issues related to Islamic financial instruments in the capital market, which causes a number of capital market instruments to face a Sharia challenge. In this article, in an analytical-descriptive way, the jurisprudential ruling of Bay' al-Aina has been re-examined, and after expressing the views of the jurists, the specific evidences surrounding it have been examined. According to the prevailing jurisprudential opinion, it is certain that the sanctity and nullity of the sale of al-Aina are reserved for the place where there are two conditions: a) we are faced with the combination of two sales contracts, one of which is cash and the other is a loan, and b) the second sale is in the first sale. be a condition The findings of the article show that the prevailing jurisprudential opinion is a point of controversy, and from the narrations in this regard, even with the mentioned conditions, the ruling of sanctity and invalidity cannot be deduced. In the end, the capital market tools that are facing the suspicion of bias have been examined; The results of the research indicate that the conventional repo, the combination of sale and lease with the condition of acquisition and sale with the addition of the option to buy and the option to sell, according to the investigation, none of them are condemned or invalidated due to the suspicion of sale of al-Ainah.

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.

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.

In this paper the price bubble contagion in two currency market and the stocks market in a six year period (2015-2021) is investigated. For this purpose, the price bubble of both markets was examined and the dates of their formation and collapse were determined by using (RADF), (SADF),(GSADF) test. Then the contagion of the bubble in the financial market of Iran was investigated using a regression model. The findings of this research showed that there was a bubble in the foreign exchange market during five periods of 2016:10:31-2017:01:21, 2017:10:03-2018:12:16,2018:12:23-2019:07:14, 2020:02:22-2021:01:17 and 2021:01:23-2021:03:19.There were four bubble periods in the stock exchange for the total price index in periods 2016:02:06-2016:04:28,2017:09:04-2017:10:10,2017:10:17-2018:04:19 and 2018:06:10-2021:03:19. Also, the results indicate that the contagion of the price bubble from the currency market to the stock exchange market is statistically significant and the contagion of the bubble has occurred between the currency market and the stock exchange market .

One of the widely used tools in risk management and portfolio optimization is derivatives. Future contracts are one of the most important derivative instruments that are used by investors and risk hedgers.In recent years, with the increase in the volume and value of the futures market transactions, the attractiveness of this market for economic operators has increased significantly.The purpose of this research is the valuation of the futures contract based on stochastic interest rate models and compare their performance with the valuation model under non-stochastic interest rate in Iran Mercantile Exchange.In this research, using the stochastic interest rate models of Vasicek (1977) and Cox, Ingersoll and Ross (1985), we value future contracts in the Iran Mercantile Exchange from the March 2021 to the August 2022. The data of futures contracts are divided into short-term and long-term groups based on the remaining time to maturity; As a result, based on the RMSE criterion and then the variance analysis test of the valuation results, it was found that there is no significant difference between the valuation models in the valuation of short-term contracts, while the valuation under the Vasicek model of long-term contracts and all contracts, performed better than other models.

Since it is not possible to accurately predict the future, most investment decisions are made with uncertain results. Recent advances in neuroscience and endocrinology have helped to reveal a biological mechanism related to economic decisions and thus potential factors related to individual differences in decision making. The data in this research was collected in the field and in the library through questionnaires and clinical tests, an invasive (serum) blood test method was used to measure hormones. The research samples include 77 people from known investors. The information has been processed and analyzed with Samart pls statistical software.The studied hormones include Testosterone, Cortisol 8 A.M., Free Testosterone and Thyroid (T3, T4 and TSH). The analyzes show that Testosterone is the only hormone that has a direct effect on optimism, decoy and risk-taking, and Free Testosterone hormone on risk-taking and decoy, T4 hormone on optimism and risk-taking, and T3 hormone on risk-taking have an inverse and significant effect on investors' behavior and the level of cortisol and TSH hormones has no significant effect on investors' behavior.

Hedging the risk caused by price volatility using options relies on an accurate and appropriate valuation of those options. Therefore, the purpose of this research is to value the options traded on the Tehran Stock Exchange using modular neural networks. The study will also compare the performance of these modular neural networks with the most renowned options valuation models, namely the Black-Scholes-Merton model and the multi-layer perceptron neural network model.

For this research, data on call options traded on the Tehran Stock Exchange from March 2018 to March 2022 were utilized. Initially, after removing outlier data, 80% of the dataset was designated as training data, while the remaining 20% was set aside as test data. To facilitate a comparison of results obtained from different models, these two subsets of data remained constant throughout the research. In this study, the theoretical prices generated by each model were compared with the market prices traded on the Tehran Stock Exchange using MSPE, RMSPE, and MAPE statistical criteria. To calculate the prediction error for the Black-Scholes-Merton model, the theoretical price of options was first obtained using its pricing formula. Subsequently, the theoretical prices derived from the Black-Scholes-Merton equation were compared with their corresponding market prices. In the neural network models, option prices were predicted using Python and its machine learning algorithms. Finally, the predicted prices from the models were compared with the market prices of the same options. To assess the significant differences between each model and the others, the Paired Sample Test of the mean percentage of errors was employed.

This research showed that, from the perspective of the RMSPE criterion, the developed neural network model with implied volatility has the lowest error and has the best performance in valuing call options across all monetary positions and periods compared to other investigated models. However, the performance of the developed multi-layer perceptron neural network model with implied volatility has been slightly better than that of its modular counterpart. Following this, the neural networks developed with historical volatility, the neural networks with discrete data, the Black-Scholes and Merton model, and the modular neural network model proposed by Gradoevich et al. (2009) have been the most accurate, respectively. From the perspective of the MAPE criterion, the developed neural network model with implied volatility has performed the best; however, among all the neural network models, the multi-layer perceptron neural network has outperformed the modular model.

Modular neural network models can outperform the Black-Scholes and Merton models. Incorporating implied volatility enhances the performance of neural networks in options valuation. However, when considering the RMSPE criterion, modular neural networks trained with historical volatility perform better than multi-layer perceptron neural networks. In contrast, for models using implied volatility, the modular neural network does not achieve better performance than the multi-layer perceptron neural network. Overall, neural networks utilizing implied volatility—whether in modular or multi-layer perceptron configurations—exhibit superior performance in long-term periods and in ITM (in-the-money) moneyness situations.

This study aims to present a novel model for predicting the future commitments of insurance companies that can adequately address the potential challenges of traditional methods. Traditionally, insurance companies use the Chain Ladder approach as a statistical tool to forecast the trend of claims development. This statistical method is favored by regulatory authorities in various countries due to its simplicity in assumptions and clear interpretation. However, certain assumptions, such as the stability of data development and linear relationships between variables, can affect the efficiency of this model when faced with internal policies or external factors like the COVID-19 pandemic. Forecasting future commitments close to reality is closely related to the financial stability of insurance companies. The amount that insurance companies allocate to meet their future obligations is identified as reserves. Calculating reserves that are less than the required amounts can pose challenges for insurance companies in fulfilling their commitments while calculating more than necessary amounts can negatively impact the financial statements of insurance companies.

In this study, a dynamic model based on machine learning algorithms is proposed. The model's output, which combines the number and timing of bodily injury accidents, plays a crucial role in calculating reserves for non-life insurance products. This model is specifically trained to predict the frequency of accidents in Vehicle Third-Party Liability Insurance. It can identify hidden patterns and non-linear, complex relationships within claims data. A Long Short-Term Memory (LSTM) neural network algorithm is employed, recognized for its strong predictive capability in time series data. The model is trained using historical data from Karafarin Insurance Company covering the years 2017 to 2021.

The performance of the model is highly related to the hyperparameters chosen for the model. Two of the most common approaches for tuning the hyperparameters are tested in this study. These Two models are grid and random search. The Root Mean Square Error (RMSE) is used as a performance metric, and it indicates that the grid search has a lower RMSE than the random search for the training data with a slight difference (16.33 versus 17.4). However, the results for the test data in the grid search have a sign of overfitting.

This study recommends using random search for tuning the hyperparameters of the model to predict the frequency of daily incidents. The evaluation of the two approaches for tuning hyperparameters indicates that random search is more suitable for working with unfamiliar data and managing overfitting situations. Overfitting occurs when the model becomes overly influenced by the training data, learning not only the actual patterns but also the noise and minor details of the data. This issue can negatively impact the model's generalization ability.

The impact of financial and economic shocks and uncertainty is not always limited to the target market and may spread to other markets as well. Empirical research results, such as those by Jurado et al. (2015) and Gabor and Gabota (2020), indicate that the contagion of cross-sectoral uncertainty and the significance of these uncertainties are not constant over time and may change. Traditional time series regression models assume that a relationship with fixed coefficients can be applied across different time periods. The misleading results of this unrealistic assumption have led to the development of dynamic models that better reflect the realities of the external world. The state-space approach is a modeling method for dynamic systems that predicts and analyzes system behavior under these modeling conditions. One of the applications of this approach is to account for structural instability in parameters and to allow coefficients to vary over time. Models of this type are known as time-varying parameter (TVP) models. This research aims to study the reaction of the financial, housing, and macroeconomic sectors in Iran to each other's shocks, with a focus on the effects of uncertainty contagion.
 
The present study is applied in terms of purpose and correlational analysis in terms of nature and method. It is post-event and utilizes past information. In this study, a library method was used to collect theoretical sources, while an archival method was employed to gather the data needed for hypothesis testing. To examine changes in cross-sectoral uncertainty contagion, the time-varying parameter vector autoregression model (TVP-VAR) is used with monthly data from January 2008 to December 2020. In this context, uncertainty indicators are calculated using GARCH models and then tested using the TVP-VAR approach, along with an analysis of variance of the generalized prediction error of total dynamic connectedness, as well as the directional dynamic connectedness of the indicator pairs.
 
The research results indicate that the primary source of uncertainty is the macroeconomic sector, which acts as the main source and transmitter of uncertainty to the other financial and housing sectors. Additionally, the housing sector is a net recipient of uncertainty from the other two sectors. The findings suggest that the contagion of uncertainty between the financial and housing sectors is bidirectional and conditionally dynamic, while the contagion of uncertainty from the macroeconomic sector to the financial and housing sectors is unidirectional.
 
According to the results, the contagion of cross-sectoral uncertainty and the significance of these uncertainties are not constant and change over time. Therefore, identifying the different channels of contagion between markets and pinpointing the source of contagion can help in selecting policies that reduce vulnerability and enhance the performance of asset portfolio risk management.

Using cryptocurrencies to hedge against the risk of various types of assets can be considered a useful feature in cryptocurrency investment. In recent years, investment in cryptocurrencies has become more common, and many people have allocated a portion of their portfolios to cryptocurrencies. Understanding the behavior and capabilities of cryptocurrencies can help investors better manage their investments. In this research, we have studied the capability of cryptocurrencies to hedge investment portfolios in Iran's economy. We wanted to determine whether cryptocurrencies can hedge investments in the stock market and gold coins. Accordingly, we have selected two popular cryptocurrencies, namely Bitcoin and Ethereum, to investigate their capability to hedge investments in the stock and gold markets in Iran.

To investigate the risk hedging of common Iranian investments using cryptocurrencies, daily data related to the Tehran Stock Exchange Index and the price of the Bahar Azadi gold coin were utilized. The daily returns of the gold coin and the stock index were calculated over a four-year period from March 2019 to April 2023. Additionally, using the exchange rate of the US dollar in the free market, the daily prices of the most used cryptocurrencies (Bitcoin and Ethereum) were collected, and their daily returns were extracted. To examine the volatility of the variables, the researchers employed the multivariate GARCH autocorrelation model. For evaluating the hedging capability of cryptocurrencies based on the minimum risk approach, they used the following three methods Constant Conditional Correlation (CCC), Dynamic Conditional Correlation (DCC), and the BEKK Diagonal Correlation Matrix.

The results showed that cryptocurrencies can be used to hedge the risk of investments in the gold coin and stock markets in Iran. It should be noted that, based on the results, the hedge ratio of Bitcoin is larger than that of Ethereum, and to hedge the risk of investments in gold coins and stocks, Bitcoin has consistently allocated a higher percentage of the portfolio compared to Ethereum. Furthermore, it was found that the Dynamic Conditional Correlation (DCC) method provided a larger average risk hedge ratio across all portfolios. On the other hand, the results of the BEKK Diagonal Correlation method exhibited more fluctuations compared to the other approaches. Additionally, the findings indicated that during the periods from April to December 2020 and from September to March 2023, alongside the significant increase in the price of the US dollar in the country, the required weight of cryptocurrencies for hedging in investment portfolios composed of Bitcoin and gold coins, Ethereum and gold coins, as well as Bitcoin and stocks, and Ethereum and stocks increased.

Bitcoin and Ethereum can hedge investments in the Tehran Stock Exchange and the Iranian Gold Coin market. It should be noted that during times of uncertainty and devaluation of the local currency against the US dollar, greater investment in cryptocurrencies is needed to hedge investments in the gold and stock markets.

This study aims to evaluate the drivers of startup financing in capital market firms through the lens of libertarian or metaphysical strategies. The goal is to develop insights that enhance the appeal of investment in startups, ultimately fostering a more attractive environment for investors interested in this sector.

This study is exploratory, with practical applications based on the analysis and data collection methods. It employs a mixed-method approach to integrate various data for comprehensive insights. In the qualitative phase, thematic analysis and link matrix analysis were conducted, involving 14 university experts in financial management. For the quantitative phase, 50 managers from capital market firms participated through reciprocal matrices and assessments to determine the data scenarios. In this step, pairwise comparison matrices were created in the form of row " " and column " " to determine the most important drivers of financing startups at the level of capital market firms. Drawing the results of the matrix in the form of a MICMAC diagram the two basic axes for scenario planning were determined.

In the qualitative phase, the findings indicated that among 12 initial studies and 6 additional studies aimed at determining key dimensions, 8 main themes were identified as factors influencing the sustainability of startup financing drivers. These themes were also validated through the Delphi process. In the quantitative phase, the results yielded four scenarios, each accompanied by explanatory statements: the Market Leadership Matrix, the Intelligent Competition Matrix, the Territory Conquest Matrix, and the Left Behind Matrix. These scenarios serve as a foundation for selecting the most advantageous approach to startup financing from the perspective of libertarian philosophical strategies within capital market firms. As a result of the quantitative analysis, the Territory Conquest scenario emerged as the most significant context for influencing startup financing drivers. This scenario, shaped by the critical thinking strategy as a philosophical foundation rooted in entrepreneurial metaphysics, offers substantial potential for fostering the growth of startups within capital market firms in the future.

The findings of this study indicate that advanced technology in the development of startups enhances their sustainability, allowing firms to capture a greater market share in the future. Capital market firms are in the process of changing their production lines, starting new production units, supplying new products to the market, or even entering international markets. According to this matrix, they should focus on technological capabilities to attract financial providers, ensuring that firms do not encounter challenges in developing their startup businesses.

The purpose of this study is to identify the threshold of systematic and non-systematic risks to the supply chain of oil, petrochemical, and chemical industries. The period of this research is 2011-2021. This research has two communities. The first society is the experts in the petrochemical and chemical industries, and the second society is the active firms in the petrochemical and chemical industries on the Tehran Stock Exchange. In this research, 99 risks affecting the supply chain of oil, petrochemical and chemical industries were investigated in the form of two categories: unsystematic risk factors (77 indicators) and systematic risk (22 indicators). Based on the expert’s opinion, using the Delphi-fuzzy type 3 approach, 23 unsystematic risk indicators and 13 systematic risk indicators were selected. Based on the principal component analysis approach, systematic and unsystematic risk components were extracted from the selected variables. The results of the PSTR model between the supply chain and systematic risk (0.353 negative impact above the threshold; 0.218 positive impact below the threshold) and unsystematic risk (0.413 negative impact above the threshold; 0.237 positive impact below the threshold) confirmed the behavior of the threshold.