University of Isfahan Journal of Asset Management and Financing 2383-1189 11 4 2023 12 22 Explaining the Dimensions of Sentiment Management for Asset Management Services Customers Using Meta-Synthesis Explaining the Dimensions of Sentiment Management for Asset Management Services Customers Using Meta-Synthesis 1 28 27667 10.22108/amf.2023.137559.1797 FA Vahid Nasehifar Associate Professor, Department of Business Administration, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran Zohreh Dehdashti Shahrokh Professor, Department of Business Administration, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran Mohammad Taghi Taghavifard Professor, Department of Operations Management and Information Technology, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran Mohsen Lalejini Ph.D. Candidate, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran Journal Article 2023 05 02 This research seeks to provide a model for explaining the dimensions of customer sentiment management of asset management services, including investment funds and portfolio-managed accounts, using meta-synthesis application. By reviewing and screening 254 papers written between 2002-2022, coding was done by studying 49 studies. Finally, the components were classified with 32 final codes as basic themes and 5 codes as organizing themes. The reliability and validity of the model were calculated by a Kappa coefficient equal to 0.78, which is at the level of excellent agreement. Since this study uses the meta-synthesis method, its innovation is a method that examines all national and international studies in this field and identifies the dimensions of the subject completely and comprehensively for the readers. According to the results, the components of customer sentiment management dimensions of asset management services were categorized with five themes including the importance of sentiment management, influencing factors on customer sentiments, sentiment characteristics, sentiment management practices, and results of sentiment management. The results can be used by managers of asset management institutions in designing strategies for managing customers' sentiments. Keywords: Customer Satisfaction, Sentiment Management, Asset Management, Meta-synthesis, Capital Market. Introduction Clients of asset management companies are people who invest in investment funds or privately managed accounts. Meanwhile, individual investors have emotional orientations (Tabatabaei, et al. 2021). Several studies such as Gutsche et al. (2023) and Heeb et al. (2022) showed that emotions play an important role in investment behaviors. In addition, Imbug et al. (2018), believe that managing sentiments and experiences is very important to attract new customers. The main problem is that due to insufficient research in the field of emotions, the managers of asset management companies in Iran do not have a framework to understand the dimensions of managing the sentiments of their customers. So, the research question is: what are the dimensions of managing the sentiments of clients of asset management services? Various research has been conducted in the field of investors' feelings and behavior in financial markets, such as the book Investor Behavior written by Baker and Riccardi (2014). In addition, Riccardi (2005) examined a large volume of literature in the field of behavioral finance and emotions, and it became a good starting point for those interested in this field. Goodell et al. (2023) also answered the question of which emotions in which conditions cause a certain effect in the market by reviewing thirty years of papers in the field of emotions and the stock market. So, the main objective of this research is to provide a framework for the optimal design of marketing strategies based on customer sentiments by identifying the dimensions of customer sentiment management of asset management services. Materials and Methods The method of this study is meta-synthesis, which is a type of literature review. The steps of this meta-synthesis are based on the seven-step method of Barroso and Sandelowski (2007). These steps consist of research question design, systematic literature review, search and selection of appropriate sources, information extraction from sources, analysis and combination of qualitative findings, quality control, and presentation of findings. In the first step, the research question was designed as follows: What are the dimensions of managing the sentiments of clients of asset management services? A systematic review of the texts with the keywords of customers' sentiment, experience, marketing, asset management, and emotions in the database was done. In this study, by examining 254 published papers and filtering the papers in terms of title, abstract, and content suitability with the subject under investigation from the years 2002 to 2022, finally studying 49 papers, three-stage coding (i.e. open, axial, and selective coding) was done. Finally, the components were categorized with 32 final codes under the title of basic themes and 5 codes under the title of organizing themes. The reliability and validity of the model were calculated through the Kappa coefficient equal to 0.78, which is at the level of excellent agreement. Research Findings In the first step of this meta-synthesis, we found 254 papers, which were screened based on issues such as title, abstract, content, and research method, and the final 49 papers were selected for review in the next stages. Finally, after analyzing these papers line by line and combining the qualitative findings, 32 basic themes and 5 organizing themes were categorized as follows. Table 1. Organizing and basic themes Basic themes Organizer themes High effectiveness of asset management service customers from emotions The importance of sentiment management Predictability of customer behavior Customer behavior control Creating value and rebuilding relationships with customers Attracting new customers Trust in the asset management company Influencing factors on customer sentiments Customer experiences The media Return on assets in asset management services Portfolio composition and asset management service risk The level of customer expectation of service quality Customer's risk-taking level Political and economic factors Financial and income profile of customers The level of knowledge and awareness of customers The influence of people around Create satisfaction Results of sentiment management Create a sense of security Build loyalty Changing customers' judgments Changing sales level and customer retention Reciprocal effect on employees Lack of stability of emotions Sentiments characteristics Spreading feelings to others Variety of positive and negative emotions Predictability of sentiment Ability to influence sentiment The complexity of managing sentiment Ways of recognizing emotions Sentiment management practices Choice of sentiment management strategy Tools for influencing emotional stimuli Effective communication to manage sentiment Discussion and Conclusions Considering that the behavior of investors originates from their emotional tendencies (Hoseini & Morshedi, 2019) and until now a framework for optimizing marketing strategies based on the sentiments of clients of asset management services has not been available to managers, the purpose of this study was to explain the dimensions of managing the sentiments of clients of asset management services. The good news is that sentiments are predictable and can be influenced by proper management (Phung, T., & Khuong, 2016). Each customer faces different emotions such as fear or avoiding regret, overconfidence, conservatism, etc. (KhoshTinat & Nadi Ghomi, 2010). According to Wiener (2009), we should first recognize these feelings and then provide an appropriate response to them. Some of the factors influencing sentiments such as customer experiences, their financial and income profile, and their level of knowledge and awareness are individual characteristics. Some others, such as the level of risk-taking, level of customer expectation, and composition and risk of the portfolio are determined by the relationship between the customer and the company. Macro factors such as political and economic factors are also beyond the control of company managers (Khalfaoui, et al. 2019). Other factors such as trust in the company, advertising, communication with customers, and the returns of the funds are completely under the control of the companies. Therefore, managers are recommended to promote their company brand and provide good services. To influence sentiments, they can use effective communication tools such as public relations and information, electronic marketing, call center, advertising, and direct communication (Bandpay, et al. 2023). Acknowledgments We are grateful to Mrs. Neda Omidi, who accompanied the researchers during the stages of this study, including the coding of themes, and to Mrs. Akram Lalejini, who worked diligently in editing the paper. This research seeks to provide a model for explaining the dimensions of customer sentiment management of asset management services, including investment funds and portfolio-managed accounts, using meta-synthesis application. By reviewing and screening 254 papers written between 2002-2022, coding was done by studying 49 studies. Finally, the components were classified with 32 final codes as basic themes and 5 codes as organizing themes. The reliability and validity of the model were calculated by a Kappa coefficient equal to 0.78, which is at the level of excellent agreement. Since this study uses the meta-synthesis method, its innovation is a method that examines all national and international studies in this field and identifies the dimensions of the subject completely and comprehensively for the readers. According to the results, the components of customer sentiment management dimensions of asset management services were categorized with five themes including the importance of sentiment management, influencing factors on customer sentiments, sentiment characteristics, sentiment management practices, and results of sentiment management. The results can be used by managers of asset management institutions in designing strategies for managing customers' sentiments. Keywords: Customer Satisfaction, Sentiment Management, Asset Management, Meta-synthesis, Capital Market. Introduction Clients of asset management companies are people who invest in investment funds or privately managed accounts. Meanwhile, individual investors have emotional orientations (Tabatabaei, et al. 2021). Several studies such as Gutsche et al. (2023) and Heeb et al. (2022) showed that emotions play an important role in investment behaviors. In addition, Imbug et al. (2018), believe that managing sentiments and experiences is very important to attract new customers. The main problem is that due to insufficient research in the field of emotions, the managers of asset management companies in Iran do not have a framework to understand the dimensions of managing the sentiments of their customers. So, the research question is: what are the dimensions of managing the sentiments of clients of asset management services? Various research has been conducted in the field of investors' feelings and behavior in financial markets, such as the book Investor Behavior written by Baker and Riccardi (2014). In addition, Riccardi (2005) examined a large volume of literature in the field of behavioral finance and emotions, and it became a good starting point for those interested in this field. Goodell et al. (2023) also answered the question of which emotions in which conditions cause a certain effect in the market by reviewing thirty years of papers in the field of emotions and the stock market. So, the main objective of this research is to provide a framework for the optimal design of marketing strategies based on customer sentiments by identifying the dimensions of customer sentiment management of asset management services. Materials and Methods The method of this study is meta-synthesis, which is a type of literature review. The steps of this meta-synthesis are based on the seven-step method of Barroso and Sandelowski (2007). These steps consist of research question design, systematic literature review, search and selection of appropriate sources, information extraction from sources, analysis and combination of qualitative findings, quality control, and presentation of findings. In the first step, the research question was designed as follows: What are the dimensions of managing the sentiments of clients of asset management services? A systematic review of the texts with the keywords of customers' sentiment, experience, marketing, asset management, and emotions in the database was done. In this study, by examining 254 published papers and filtering the papers in terms of title, abstract, and content suitability with the subject under investigation from the years 2002 to 2022, finally studying 49 papers, three-stage coding (i.e. open, axial, and selective coding) was done. Finally, the components were categorized with 32 final codes under the title of basic themes and 5 codes under the title of organizing themes. The reliability and validity of the model were calculated through the Kappa coefficient equal to 0.78, which is at the level of excellent agreement. Research Findings In the first step of this meta-synthesis, we found 254 papers, which were screened based on issues such as title, abstract, content, and research method, and the final 49 papers were selected for review in the next stages. Finally, after analyzing these papers line by line and combining the qualitative findings, 32 basic themes and 5 organizing themes were categorized as follows. Table 1. Organizing and basic themes Basic themes Organizer themes High effectiveness of asset management service customers from emotions The importance of sentiment management Predictability of customer behavior Customer behavior control Creating value and rebuilding relationships with customers Attracting new customers Trust in the asset management company Influencing factors on customer sentiments Customer experiences The media Return on assets in asset management services Portfolio composition and asset management service risk The level of customer expectation of service quality Customer's risk-taking level Political and economic factors Financial and income profile of customers The level of knowledge and awareness of customers The influence of people around Create satisfaction Results of sentiment management Create a sense of security Build loyalty Changing customers' judgments Changing sales level and customer retention Reciprocal effect on employees Lack of stability of emotions Sentiments characteristics Spreading feelings to others Variety of positive and negative emotions Predictability of sentiment Ability to influence sentiment The complexity of managing sentiment Ways of recognizing emotions Sentiment management practices Choice of sentiment management strategy Tools for influencing emotional stimuli Effective communication to manage sentiment Discussion and Conclusions Considering that the behavior of investors originates from their emotional tendencies (Hoseini & Morshedi, 2019) and until now a framework for optimizing marketing strategies based on the sentiments of clients of asset management services has not been available to managers, the purpose of this study was to explain the dimensions of managing the sentiments of clients of asset management services. The good news is that sentiments are predictable and can be influenced by proper management (Phung, T., & Khuong, 2016). Each customer faces different emotions such as fear or avoiding regret, overconfidence, conservatism, etc. (KhoshTinat & Nadi Ghomi, 2010). According to Wiener (2009), we should first recognize these feelings and then provide an appropriate response to them. Some of the factors influencing sentiments such as customer experiences, their financial and income profile, and their level of knowledge and awareness are individual characteristics. Some others, such as the level of risk-taking, level of customer expectation, and composition and risk of the portfolio are determined by the relationship between the customer and the company. Macro factors such as political and economic factors are also beyond the control of company managers (Khalfaoui, et al. 2019). Other factors such as trust in the company, advertising, communication with customers, and the returns of the funds are completely under the control of the companies. Therefore, managers are recommended to promote their company brand and provide good services. To influence sentiments, they can use effective communication tools such as public relations and information, electronic marketing, call center, advertising, and direct communication (Bandpay, et al. 2023). Acknowledgments We are grateful to Mrs. Neda Omidi, who accompanied the researchers during the stages of this study, including the coding of themes, and to Mrs. Akram Lalejini, who worked diligently in editing the paper.

https://amf.ui.ac.ir/article_27667_531d03b3a5dd8eeba8b4236bd44b970e.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 11 4 2023 12 22 Healthcare Supply Chain Financing through Public-Private Partnership: A Strategic Analysis of Drivers Healthcare Supply Chain Financing through Public-Private Partnership: A Strategic Analysis of Drivers 29 46 28253 10.22108/amf.2024.139148.1825 FA Mohammad Javad Ganji Arjenaki M. A. Graduate, Department of Business Administration, Faculty of Financial Sciences, Management and Entrepreneurship, University of Kashan, Kashan, Iran 0009-0006-4183-1128 Esmaeil Mazroui Nasrabadi Assistant Professor, Department of Business Administration, Faculty of Financial Sciences, Management and Entrepreneurship, University of Kashan, Kashan, Iran Journal Article 2023 09 16 Funding in the healthcare supply chain has always faced challenges, and the challenges of this industry have increased due to inflation and the government's financial problems. One of the most important methods of financing in the healthcare supply chain is a public-private partnership, therefore it is important to identify the drivers of this method of financing. According to the research gap in the field of identification of drivers in this supply chain, modeling, and analysis of their scenario, this study has been carried out to cover this research gap. This research has been done in two qualitative and quantitative stages. In the qualitative stage, with semi-structured interviews and thematic analysis, drives were identified, and in the quantitative stage, using a researcher-made questionnaire and fuzzy cognitive map, modeling, and scenario analysis of drives were done. The results of the qualitative stage show 23 drives that were grouped into 8 categories. The results of the fuzzy cognitive map indicate the independence of the ‘risk transfer’ drive, the dependence on the ‘profit’ drive, and the highest degree of centrality of the ‘hardware upgrade’ drive. The analysis of forward and backward scenarios shows the high importance of ‘asset management and financing’ and ‘infrastructure improvement’ drivers. According to the findings of the research, it is suggested to formulate a road map for the development of infrastructures, pay according to the quality, and determine the correction factor for the improvement of important drivers. Funding in the healthcare supply chain has always faced challenges, and the challenges of this industry have increased due to inflation and the government's financial problems. One of the most important methods of financing in the healthcare supply chain is a public-private partnership, therefore it is important to identify the drivers of this method of financing. According to the research gap in the field of identification of drivers in this supply chain, modeling, and analysis of their scenario, this study has been carried out to cover this research gap. This research has been done in two qualitative and quantitative stages. In the qualitative stage, with semi-structured interviews and thematic analysis, drives were identified, and in the quantitative stage, using a researcher-made questionnaire and fuzzy cognitive map, modeling, and scenario analysis of drives were done. The results of the qualitative stage show 23 drives that were grouped into 8 categories. The results of the fuzzy cognitive map indicate the independence of the ‘risk transfer’ drive, the dependence on the ‘profit’ drive, and the highest degree of centrality of the ‘hardware upgrade’ drive. The analysis of forward and backward scenarios shows the high importance of ‘asset management and financing’ and ‘infrastructure improvement’ drivers. According to the findings of the research, it is suggested to formulate a road map for the development of infrastructures, pay according to the quality, and determine the correction factor for the improvement of important drivers.

https://amf.ui.ac.ir/article_28253_547549facbf37e6b288fd0c98eb481c0.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 11 4 2023 12 22 Option Pricing Error: Evidence from Nonlinear Markets based on Probabilistic Neural Networks and Multilayer Perceptron Option Pricing Error: Evidence from Nonlinear Markets based on Probabilistic Neural Networks and Multilayer Perceptron 47 64 28254 10.22108/amf.2024.139784.1838 FA Mohammad Hossein Seifi MSc Student, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran Elham Dastranj Associate Professor, Department of Pure Mathematics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran Abdolmajid Abdolbaghi Assistant Professor, Department of Management, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Sanaz Lamei Assistant Professor, Department of Mathematical Sciences, University of Guilan, Rasht, Iran Journal Article 2023 11 13 This sudy aims to compare the pricing error of Barles-Soner and Bakstein-Howison equation, in the S&P500 index option market. Option pricing equations are solved using Lie algebra. Using the historical data of the S&P500 index from August 18, 2022, to August 18, 2023, the price of this asset has been calculated with each model considered. In the sequel, the obtained data are classified using multilayer Perceptron and Probabilistic neural networks. The networks show which model is closest to the real market. In addition, the prices obtained from Lie algebra have been compared with the actual values of the options in the market. PNN and MLP have been tested with statistical data after August 18, 2023. Two assumed models were priced with the same data and then compared with the real market. In testing the networks, MLP put 60% of the test data and PNN put all the data into the Barels-Soner category. By calculating the difference between the results of the Lie groups and the real data, 80% of the data were less different from the Barrels-Soner model. With the results obtained in S&P option pricing, the Barels-Soner model has less error than other models. Keywords: Nonlinear Markets, Lie Groups, Neural Networks, Options. Introduction Options are created to manage risk, control, and prevent loss. In trading, one party's gain means the other party's loss. To eliminate these gains and losses, option pricing must be fair. In other words, the price must be determined so that neither party suffers a loss. In this study, two nonlinear models Barles-Soner (Barles, 1998) and Bakstein-Howison (Bakstein, 2003) were used. By including transaction costs, these two models are closer to market reality than the Black-Scholes model. According to the two models, European option pricing was performed using Lie and symmetric groups. In this method, by order reduction, the partial differential equation is converted into a solvable ordinary differential equation. After evaluating two nonlinear Black-Scholes models, their responses are classified using a Probabilistic neural network (Specht, 1990) and a multilayer Perceptron, and it is predicted that the market is closer to which model. In the section part, after introducing the two nonlinear Black-Scholes models, the basic concepts, and theoretical foundations of Lie groups, Probabilistic neural networks, and Perceptron are stated. In the second part, the method used in the research is explained. In this section, after finding the exact solutions of the equations of Barles-Soner (1998) and Bakstein-Howison (2003), the European the S&P500 index option is priced. The findings obtained from this pricing are presented in the next section and finally, the results obtained in this research are presented in the last section. Materials and Methods The pricing of the European option on the S&P500 index has been done using false groups, based on the nonlinear equations of Barles-Soner (1998) and Bakstein-Howison (2003). Next, Perceptron and Probabilistic neural networks were explained. To find the exact solution, Lie and symmetry groups were used. In this method, by order reduction, the partial differential equations are converted into ordinary differential equations. Then, an exact solution of this equation is provided by solving ordinary differential equations. Then by flowing the solutions, other new solutions were found (Dastranj & Hejazi, 2017). Neural networks consist of three layers. The three main layers of all models are the input layer, hidden layer, and output layer. In some models, the hidden layer is divided into multiple layers. Each layer has several nodes whose number is tested in the hidden layer by trial and error. All nodes in each layer are connected to the next and previous layers. This relationship is established by the weights being multiplied by the previous layer's output, and the result is considered as the input of the current layer. The most important process in a neural network is learning and training the network. The goal of training the network is to accurately determine the weights so that the network output is closer to the true value of the output (Hagan, 1997). After pricing with the solutions obtained from Lie algebra, we classify the data using neural networks to determine which model is closest to the actual market price. Findings Table 1 presents the results of market model prediction by comparing Lie Algebra Pricing, PNN, and MLP. Table 1. Market Model Prediction by Comparing Lie Algebra Pricing, PNN, and MLP x t Market price Barles-Soner Error Bakstein-Howison Error Lie algebra PNN MLP 4467.71 0.087649 2488.09 2597.26 109.17 2644.69 156.60 0 0 0 4404.33 0.087302 2446.57 2596.79 150.22 2607.17 160.60 0 0 0 4518.44 0.134387 2511.83 2602.91 91.08 2674.72 162.89 0 0 0 4399.77 0.114173 2379.87 2599.80 219.93 2604.47 224.60 0 0 1 4405.71 0.186508 2405 2608.06 203.06 2607.99 202.99 1 0 1 In Table 1, x is the S&P500 index and t is the remaining time until the option expires. Using the following equation and for the values of x and t presented in Table 1, pricing was done under the Barles-Soner (1998) model, the result of which is presented in the column related to Barles-Soner (1998). In the Bakstein-Howison column, the pricing solutions of the Bakstein-Howison (2003) are described with the following equation. Finally, the differences between the solutions under the two mentioned models have been compared with the real market prices, and the results are presented in the column related to Lie algebra. When the Barles-Soner (1998) price is closer to the market price, zero is placed in the table, and if the Bakstein-Howison (2003) price is close to it, one is placed. PNN and MLP columns show the results of Probabilistic neural networks and multilayer Perceptron, respectively. In these two columns, the one in the table indicates that the data is in the Bakstein-Howison (2003) category, and the zero is used to indicate that the data belongs to the Barles-Soner (1998) category. Discussion and Conclusions First, using Lie and symmetric groups for two nonlinear models, Barles-Soner (1998) and Bakstein-Howison (2003), European option pricing was performed. After pricing the S&P500 index between August 18, 2022, and August 18, 2023, a multi-layer perceptron and probabilistic neural network were trained and the data were classified into two classes, Barles -Soner (1998) and Bakstein-Howison (2003). Then, to find the model closest to the real market, the network is tested with 5 data points. Additionally, the answers obtained from the Lie algebra are evaluated using test data, and their differences from the real data are calculated to determine which answer is closest to the real data. The Perceptron neural network assumes that 3 out of 5 data items belong to the Barles-Soner (1998) model and the Probabilistic neural network places those 5 data items into the Barles-Soner (1998) category. By calculating the difference between the Lie group's response and the actual market price, 4 out of 5 data are close to the Barles-Soner (1998) model. Therefore, according to the results obtained, the S&P500 options market with a maturity of one year and an exercise price of 2,000 USD is close to the Barles-Soner (1998) model. This sudy aims to compare the pricing error of Barles-Soner and Bakstein-Howison equation, in the S&P500 index option market. Option pricing equations are solved using Lie algebra. Using the historical data of the S&P500 index from August 18, 2022, to August 18, 2023, the price of this asset has been calculated with each model considered. In the sequel, the obtained data are classified using multilayer Perceptron and Probabilistic neural networks. The networks show which model is closest to the real market. In addition, the prices obtained from Lie algebra have been compared with the actual values of the options in the market. PNN and MLP have been tested with statistical data after August 18, 2023. Two assumed models were priced with the same data and then compared with the real market. In testing the networks, MLP put 60% of the test data and PNN put all the data into the Barels-Soner category. By calculating the difference between the results of the Lie groups and the real data, 80% of the data were less different from the Barrels-Soner model. With the results obtained in S&P option pricing, the Barels-Soner model has less error than other models. Keywords: Nonlinear Markets, Lie Groups, Neural Networks, Options. Introduction Options are created to manage risk, control, and prevent loss. In trading, one party's gain means the other party's loss. To eliminate these gains and losses, option pricing must be fair. In other words, the price must be determined so that neither party suffers a loss. In this study, two nonlinear models Barles-Soner (Barles, 1998) and Bakstein-Howison (Bakstein, 2003) were used. By including transaction costs, these two models are closer to market reality than the Black-Scholes model. According to the two models, European option pricing was performed using Lie and symmetric groups. In this method, by order reduction, the partial differential equation is converted into a solvable ordinary differential equation. After evaluating two nonlinear Black-Scholes models, their responses are classified using a Probabilistic neural network (Specht, 1990) and a multilayer Perceptron, and it is predicted that the market is closer to which model. In the section part, after introducing the two nonlinear Black-Scholes models, the basic concepts, and theoretical foundations of Lie groups, Probabilistic neural networks, and Perceptron are stated. In the second part, the method used in the research is explained. In this section, after finding the exact solutions of the equations of Barles-Soner (1998) and Bakstein-Howison (2003), the European the S&P500 index option is priced. The findings obtained from this pricing are presented in the next section and finally, the results obtained in this research are presented in the last section. Materials and Methods The pricing of the European option on the S&P500 index has been done using false groups, based on the nonlinear equations of Barles-Soner (1998) and Bakstein-Howison (2003). Next, Perceptron and Probabilistic neural networks were explained. To find the exact solution, Lie and symmetry groups were used. In this method, by order reduction, the partial differential equations are converted into ordinary differential equations. Then, an exact solution of this equation is provided by solving ordinary differential equations. Then by flowing the solutions, other new solutions were found (Dastranj & Hejazi, 2017). Neural networks consist of three layers. The three main layers of all models are the input layer, hidden layer, and output layer. In some models, the hidden layer is divided into multiple layers. Each layer has several nodes whose number is tested in the hidden layer by trial and error. All nodes in each layer are connected to the next and previous layers. This relationship is established by the weights being multiplied by the previous layer's output, and the result is considered as the input of the current layer. The most important process in a neural network is learning and training the network. The goal of training the network is to accurately determine the weights so that the network output is closer to the true value of the output (Hagan, 1997). After pricing with the solutions obtained from Lie algebra, we classify the data using neural networks to determine which model is closest to the actual market price. Findings Table 1 presents the results of market model prediction by comparing Lie Algebra Pricing, PNN, and MLP. Table 1. Market Model Prediction by Comparing Lie Algebra Pricing, PNN, and MLP x t Market price Barles-Soner Error Bakstein-Howison Error Lie algebra PNN MLP 4467.71 0.087649 2488.09 2597.26 109.17 2644.69 156.60 0 0 0 4404.33 0.087302 2446.57 2596.79 150.22 2607.17 160.60 0 0 0 4518.44 0.134387 2511.83 2602.91 91.08 2674.72 162.89 0 0 0 4399.77 0.114173 2379.87 2599.80 219.93 2604.47 224.60 0 0 1 4405.71 0.186508 2405 2608.06 203.06 2607.99 202.99 1 0 1 In Table 1, x is the S&P500 index and t is the remaining time until the option expires. Using the following equation and for the values of x and t presented in Table 1, pricing was done under the Barles-Soner (1998) model, the result of which is presented in the column related to Barles-Soner (1998). In the Bakstein-Howison column, the pricing solutions of the Bakstein-Howison (2003) are described with the following equation. Finally, the differences between the solutions under the two mentioned models have been compared with the real market prices, and the results are presented in the column related to Lie algebra. When the Barles-Soner (1998) price is closer to the market price, zero is placed in the table, and if the Bakstein-Howison (2003) price is close to it, one is placed. PNN and MLP columns show the results of Probabilistic neural networks and multilayer Perceptron, respectively. In these two columns, the one in the table indicates that the data is in the Bakstein-Howison (2003) category, and the zero is used to indicate that the data belongs to the Barles-Soner (1998) category. Discussion and Conclusions First, using Lie and symmetric groups for two nonlinear models, Barles-Soner (1998) and Bakstein-Howison (2003), European option pricing was performed. After pricing the S&P500 index between August 18, 2022, and August 18, 2023, a multi-layer perceptron and probabilistic neural network were trained and the data were classified into two classes, Barles -Soner (1998) and Bakstein-Howison (2003). Then, to find the model closest to the real market, the network is tested with 5 data points. Additionally, the answers obtained from the Lie algebra are evaluated using test data, and their differences from the real data are calculated to determine which answer is closest to the real data. The Perceptron neural network assumes that 3 out of 5 data items belong to the Barles-Soner (1998) model and the Probabilistic neural network places those 5 data items into the Barles-Soner (1998) category. By calculating the difference between the Lie group's response and the actual market price, 4 out of 5 data are close to the Barles-Soner (1998) model. Therefore, according to the results obtained, the S&P500 options market with a maturity of one year and an exercise price of 2,000 USD is close to the Barles-Soner (1998) model.

https://amf.ui.ac.ir/article_28254_bba52d6228b665a485efc224ae1f81f4.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 11 4 2023 12 22 Expansion of the Markowitz Model in Portfolio Optimization Considering Realistic Constraints Expansion of the Markowitz Model in Portfolio Optimization Considering Realistic Constraints 65 92 28246 10.22108/amf.2024.137329.1793 FA Hossein Alizadeh MSc, Department of Industrial Engineering and Futures Studies, Faculty of Engineering, University of Isfahan, Isfahan, Iran Kamran Kianfar Assistant Professor, Department of Industrial Engineering and Futures Studies, Faculty of Engineering, University of Isfahan, Isfahan, Iran Journal Article 2023 04 12 This research aims to expand the Markowitz model in a way that aligns more closely with real-world conditions, considering various fundamental analysis factors and market constraints. Conducted on the Tehran Stock Exchange, this study utilizes two measures, semi-variance and mean absolute deviations, alongside the variance measure in the Markowitz model to better estimate risk levels. In addition, several constraints such as cardinality constraint, threshold constraint, and segmentation constraint are employed to bring the results of the Markowitz-based model closer to reality. To ensure that the stock return metric is not solely based on stock price changes, this research incorporates nine important fundamental analysis metrics in filtering company stocks and as a return metric in the Markowitz model. Due to the computational complexity of the mathematical programming model, sample problems were also solved using the Harmony Search algorithm. The results indicate that the mathematical model performs better in terms of the distance from the ideal point efficiency metric, while the harmony search algorithm excels in uniformity metrics, exploring diverse solutions, and solution time. Increasing the range of cardinality and threshold constraints results in selecting more stocks in the portfolio, and simultaneously, the risk and return objective functions will improve concurrently. Keywords: Markowitz Model, Stock Portfolio Selection, Fundamental Analysis, Stock Risk Management, Harmony Search Algorithm. Introduction In this study, the semi-variance and mean absolute deviation models are used alongside the mean-variance model for comparison. Developed models for optimizing stock portfolios heavily rely on realistic constraints. Budget constraints, cardinality constraints, threshold constraints, and segmentation constraints are among the most crucial constraints in the stock market, which were utilized in this research for portfolio selection (Mehrjerdi & Rasaei, 2013). Besides return metrics, indicators such as P/E (price to earnings per share), ROE (return on equity), six-month turnover rate, and company total asset ratio are used in the current research for initial stock screening, followed by metrics such as P/S (price per share to sales), ROA (return on assets), quick ratio, and turnover to total market value in the objective functions of the Markowitz model (Asgarnezhad, 2018). This study, conducted on the Tehran Stock Exchange, utilizes nine important and strategic industry groups that have a significant impact on the overall index. The study period covers the second six months of the year 2022, and questionnaires were completed by capital market experts and brokers from the Mashhad Mofid brokerage. The main question of this study in the field of financial optimization is how to expand the Markowitz (1952) model to better align with real-world conditions. Alongside the main question, several subsidiary questions arise, including: 1) How can stocks be selected based on realistic constraints? 2) How is the balance between risk and return established in portfolio selection? 3) Which fundamental analysis indicators are more important in stock portfolio selection on the Tehran Stock Exchange? Materials and Methods The research aims to minimize portfolio investment risk and maximize expected portfolio return. The first objective seeks to select a combination of stocks with the least investment risk, utilizing three different measures: variance, semi-variance, and mean absolute deviation. Due to the abundance of factors and sub-factors, two questionnaires were designed for financial market specialists to prioritize factors through analytic hierarchy process (AHP) analysis. Fifteen experts with over five years of experience in financial, accounting, or managerial fields were selected through purposive sampling. In the proposed mathematical programming model, sub-factors with higher importance within each group are incorporated into the objective function, while less important ones are used as filters before entering the model. For instance, ROA, P/S, quick ratio, market value of the company to industry, and percentage of companies' operating profit are included in the objective function, while ROE, P/E, turnover rate, and percentage of total assets serve as filters. Due to the nonlinearity and NP-hardness of the portfolio optimization problem, traditional mathematical programming models may not yield solutions within a reasonable timeframe, especially for large-scale problem instances. Hence, the harmony search algorithm is employed to reduce solution time and approximate the optimal solution. The algorithm is implemented in MATLAB software, where a solution string representing the investment proportion in each stock is defined, allowing for the quick derivation of other variable values in the mathematical programming model. Research Findings The results of the study indicate that all 15 questionnaires had inconsistency rates lower than 0.1, indicating a good level of consistency. Therefore, the geometric mean was calculated from these 15 questionnaires. In the questionnaires, factors influencing the five selected factors were compared with each other (See Table 1). Table 1. Results of Expert Opinions in the Questionnaire for Selected Factors Factor Sub-factor (average of weight) Profitability ROA (0.392) ROE (0.374) Net Profit Margin on Sales (0.127) Operating Expense Ratio (0.108) Valuation P/S (0.302) P/E (0.301) Ln(A/B) (0.162) Ln(B/P) (0.124) Liquidity Asset Liability Ratio (0.388) Quick Ratio (0.374) Equity to Dept Ratio (0.125) Current Ratio (0.113) Volume Turnover to total market 3-month (0.447) Turnover to total market 1-month (0.260) Turnover rate 3-month (0.178) Turnover rate 1-month (0.114) Growth Total assets (0.394) Operating profit (0.320) Gross profit (0.286) All necessary data have been obtained from www.tsetmc.com and www.codal.ir websites and have been incorporated into the filters. To compare the results of the exact solution method and the harmony search metaheuristic, different sizes of sample problems need to be defined. For this purpose, sample groups N10 to N55, consisting of 10 to 55 stocks, have been defined, including categories such as oil derivatives, basic metals, chemical products, automobiles, metal extraction, food products, investments, pharmaceuticals, and banks. In the N30 group, the GAMS software requires a minimum solution time of 560 seconds, while the harmony search algorithm needs only 33 seconds to obtain local optimal points. A comparison between the two solution methods is illustrated in Figure 1, demonstrating that the SEMICOV objective function performs best for risk calculation in both exact and metaheuristic solution methods. Both figures exhibit robustness, and the solutions are well dispersed in the space. Figure 1. Pareto Charts of Three Objective Functions for Group N30 The study compares the exact method and the harmony search algorithm for optimizing stock portfolios. Results show the harmony search method's efficiency, especially in larger dimensions, despite slight differences with the exact method. The SEMICOV criterion performs well, but the MAD criterion shows instability. Additionally, the harmony search algorithm proves superior in uniformity, scalability, and solution time. Sensitivity analysis highlights the importance of parameter variations in portfolio optimization. Discussion of Results and Conclusion The results revealed that the semi-variance objective yielded better results in both the exact solution method and the harmony search algorithm in most groups of the test instances. The harmony search algorithm provides a good approximation of the optimal solution in a much shorter time compared to the mathematical model. The harmony search algorithm performed better in terms of uniformity and spread metrics of Pareto points, but the exact solution method was superior in calculating the distance from the ideal point metric. The sensitivity analysis conducted on the N30 group highlighted the significant impact of altering constraint selection ranges on the objective function and, consequently, on the stock portfolio. Initially, expanding the ranges of both the cardinality and threshold constraints led to the inclusion of stocks with higher returns and lower risks at the Pareto frontier across all risk assessment metrics. Specifically, in the semi-variance objective function, the optimal stock portfolio was found in subgroup with a return of 0.84 and a corresponding risk of 0.146. As the range decreased in subgroup , returns diminished to 0.526 with a risk of 0.15. In subgroup , where both constraints became more stringent, returns reached 0.284 with a risk value of 0.16. Consequently, the analysis concluded that broader constraint ranges corresponded to lower risks and higher returns, and vice versa. The applications of this research are for individual shareholders, financial institutions, investment funds, and portfolio managers who can use this research to improve their returns and reduce their trading risk. This research aims to expand the Markowitz model in a way that aligns more closely with real-world conditions, considering various fundamental analysis factors and market constraints. Conducted on the Tehran Stock Exchange, this study utilizes two measures, semi-variance and mean absolute deviations, alongside the variance measure in the Markowitz model to better estimate risk levels. In addition, several constraints such as cardinality constraint, threshold constraint, and segmentation constraint are employed to bring the results of the Markowitz-based model closer to reality. To ensure that the stock return metric is not solely based on stock price changes, this research incorporates nine important fundamental analysis metrics in filtering company stocks and as a return metric in the Markowitz model. Due to the computational complexity of the mathematical programming model, sample problems were also solved using the Harmony Search algorithm. The results indicate that the mathematical model performs better in terms of the distance from the ideal point efficiency metric, while the harmony search algorithm excels in uniformity metrics, exploring diverse solutions, and solution time. Increasing the range of cardinality and threshold constraints results in selecting more stocks in the portfolio, and simultaneously, the risk and return objective functions will improve concurrently. Keywords: Markowitz Model, Stock Portfolio Selection, Fundamental Analysis, Stock Risk Management, Harmony Search Algorithm. Introduction In this study, the semi-variance and mean absolute deviation models are used alongside the mean-variance model for comparison. Developed models for optimizing stock portfolios heavily rely on realistic constraints. Budget constraints, cardinality constraints, threshold constraints, and segmentation constraints are among the most crucial constraints in the stock market, which were utilized in this research for portfolio selection (Mehrjerdi & Rasaei, 2013). Besides return metrics, indicators such as P/E (price to earnings per share), ROE (return on equity), six-month turnover rate, and company total asset ratio are used in the current research for initial stock screening, followed by metrics such as P/S (price per share to sales), ROA (return on assets), quick ratio, and turnover to total market value in the objective functions of the Markowitz model (Asgarnezhad, 2018). This study, conducted on the Tehran Stock Exchange, utilizes nine important and strategic industry groups that have a significant impact on the overall index. The study period covers the second six months of the year 2022, and questionnaires were completed by capital market experts and brokers from the Mashhad Mofid brokerage. The main question of this study in the field of financial optimization is how to expand the Markowitz (1952) model to better align with real-world conditions. Alongside the main question, several subsidiary questions arise, including: 1) How can stocks be selected based on realistic constraints? 2) How is the balance between risk and return established in portfolio selection? 3) Which fundamental analysis indicators are more important in stock portfolio selection on the Tehran Stock Exchange? Materials and Methods The research aims to minimize portfolio investment risk and maximize expected portfolio return. The first objective seeks to select a combination of stocks with the least investment risk, utilizing three different measures: variance, semi-variance, and mean absolute deviation. Due to the abundance of factors and sub-factors, two questionnaires were designed for financial market specialists to prioritize factors through analytic hierarchy process (AHP) analysis. Fifteen experts with over five years of experience in financial, accounting, or managerial fields were selected through purposive sampling. In the proposed mathematical programming model, sub-factors with higher importance within each group are incorporated into the objective function, while less important ones are used as filters before entering the model. For instance, ROA, P/S, quick ratio, market value of the company to industry, and percentage of companies' operating profit are included in the objective function, while ROE, P/E, turnover rate, and percentage of total assets serve as filters. Due to the nonlinearity and NP-hardness of the portfolio optimization problem, traditional mathematical programming models may not yield solutions within a reasonable timeframe, especially for large-scale problem instances. Hence, the harmony search algorithm is employed to reduce solution time and approximate the optimal solution. The algorithm is implemented in MATLAB software, where a solution string representing the investment proportion in each stock is defined, allowing for the quick derivation of other variable values in the mathematical programming model. Research Findings The results of the study indicate that all 15 questionnaires had inconsistency rates lower than 0.1, indicating a good level of consistency. Therefore, the geometric mean was calculated from these 15 questionnaires. In the questionnaires, factors influencing the five selected factors were compared with each other (See Table 1). Table 1. Results of Expert Opinions in the Questionnaire for Selected Factors Factor Sub-factor (average of weight) Profitability ROA (0.392) ROE (0.374) Net Profit Margin on Sales (0.127) Operating Expense Ratio (0.108) Valuation P/S (0.302) P/E (0.301) Ln(A/B) (0.162) Ln(B/P) (0.124) Liquidity Asset Liability Ratio (0.388) Quick Ratio (0.374) Equity to Dept Ratio (0.125) Current Ratio (0.113) Volume Turnover to total market 3-month (0.447) Turnover to total market 1-month (0.260) Turnover rate 3-month (0.178) Turnover rate 1-month (0.114) Growth Total assets (0.394) Operating profit (0.320) Gross profit (0.286) All necessary data have been obtained from www.tsetmc.com and www.codal.ir websites and have been incorporated into the filters. To compare the results of the exact solution method and the harmony search metaheuristic, different sizes of sample problems need to be defined. For this purpose, sample groups N10 to N55, consisting of 10 to 55 stocks, have been defined, including categories such as oil derivatives, basic metals, chemical products, automobiles, metal extraction, food products, investments, pharmaceuticals, and banks. In the N30 group, the GAMS software requires a minimum solution time of 560 seconds, while the harmony search algorithm needs only 33 seconds to obtain local optimal points. A comparison between the two solution methods is illustrated in Figure 1, demonstrating that the SEMICOV objective function performs best for risk calculation in both exact and metaheuristic solution methods. Both figures exhibit robustness, and the solutions are well dispersed in the space. Figure 1. Pareto Charts of Three Objective Functions for Group N30 The study compares the exact method and the harmony search algorithm for optimizing stock portfolios. Results show the harmony search method's efficiency, especially in larger dimensions, despite slight differences with the exact method. The SEMICOV criterion performs well, but the MAD criterion shows instability. Additionally, the harmony search algorithm proves superior in uniformity, scalability, and solution time. Sensitivity analysis highlights the importance of parameter variations in portfolio optimization. Discussion of Results and Conclusion The results revealed that the semi-variance objective yielded better results in both the exact solution method and the harmony search algorithm in most groups of the test instances. The harmony search algorithm provides a good approximation of the optimal solution in a much shorter time compared to the mathematical model. The harmony search algorithm performed better in terms of uniformity and spread metrics of Pareto points, but the exact solution method was superior in calculating the distance from the ideal point metric. The sensitivity analysis conducted on the N30 group highlighted the significant impact of altering constraint selection ranges on the objective function and, consequently, on the stock portfolio. Initially, expanding the ranges of both the cardinality and threshold constraints led to the inclusion of stocks with higher returns and lower risks at the Pareto frontier across all risk assessment metrics. Specifically, in the semi-variance objective function, the optimal stock portfolio was found in subgroup with a return of 0.84 and a corresponding risk of 0.146. As the range decreased in subgroup , returns diminished to 0.526 with a risk of 0.15. In subgroup , where both constraints became more stringent, returns reached 0.284 with a risk value of 0.16. Consequently, the analysis concluded that broader constraint ranges corresponded to lower risks and higher returns, and vice versa. The applications of this research are for individual shareholders, financial institutions, investment funds, and portfolio managers who can use this research to improve their returns and reduce their trading risk.

https://amf.ui.ac.ir/article_28246_08ef1c2cfb83d0308ac02dfd044c1b09.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 11 4 2023 12 22 Identifying Production Financing with Emphasis on the Debt Market in Iran Identifying Production Financing with Emphasis on the Debt Market in Iran 93 120 28314 10.22108/amf.2024.137439.1795 FA Mohammad Solgi Assistant Professor, Department of Islamic Financial Management, Faculty of Management, Imam Hussein University, Tehran, Iran 0000-0002-8635-631X Seed Morteza Nazari M.A., Department of Management and Insurance, Faculty of Management, University of Tehran, Tehran, Iran Journal Article 2023 05 26 An efficient financial system leads to the aggregation and optimal allocation of resources in the economy. In advanced financial markets such as the United States, the economy is mainly financed through debt securities and stocks. This is despite the fact that in Iran, a small part of financing is done through debt securities and shares, and most of it is done through the banking system. The purpose of this study is to analyze the financing system of the country with an emphasis on the debt market. To achieve the above goal, a combined quantitative and qualitative method was used. At first, using the qualitative method of semi-structured interviews with selected experts, the factors of the lack of development of the debt market in the country were identified, and then the relationships between the factors were identified through the ISM method, and the identified factors were classified using MICMAC analysis. In this research, 24 factors were identified as obstacles to the development of the debt market, the highest-level factors were interest rate control by supervisory institutions, the existence of the government as an active member in the debt market, and international sanctions of the country. Keywords: Debt Market, Production Financing, Islamic Securities, Interpretive Structural Modeling (ISM). Introduction Sustainable growth and development is the main economic ideal in any country. The most important criterion for determining and measuring economic growth, GDP growth, and the main driver of production is the amount of investment. Several factors including monetary and financial policies and different methods of financing can affect the amount and volume of investments. An efficient financial system leads to the aggregation and optimal allocation of resources in the economy. Considering the role of the financial system in the functioning and economic growth, studies in the field of financial development have found a special place in the economic literature. As the economy is in the early stages of development, due to the lack of proper infrastructure for market activity, the financial system tends to be bank-oriented. In Iran, banks are mainly responsible for financing, and the capital market has not played a significant role in this process. The deeper and more developed the financial market is the companies, and investors will be able to choose the optimal ratio of debt to finance their investment projects, and this will lead to the expansion of economic opportunities, income distribution, and the reduction of income inequalities. Finally, it leads to economic growth in the society. In most advanced financing systems, the major part of financing is done via debt instruments that are issued through the capital market. Therefore, the purpose of this study is to analyze the damages of the financing system of the country in the sector of debt market development. Materials and Methods In this study, to achieve the goals, in the first stage, a literature review and identification of patterns and dimensions were done. In the next step, using a semi-structured interview, the best criteria and their relationship were identified in each dimension. In the last stage of the research model, the relationship model between the factors was identified using the interpretive structural modeling (ISM) technique. Findings By reviewing the literature on the identified factors and by using the in-depth interview technique, the identified factors were completed. Finally, by using the structural interpretive modeling technique, the relationship between patterns was determined. The factors and their leveling are shown in Figure 1: Mismatch between inflation and bond yields Absence of active marketer in the published securities in the market Easier financing through the banking system than issuing debt securities Low depth of the secondary market of debt securities The complexity and time-consuming process of security issuance in Iran's debt market Level 1 Low savings ratio in the country's economy Investors' lack of confidence in strict monitoring of issuers Lack of culture for the issuance of debt securities among issuers Lack of practical financial knowledge among publishers' managers The possibility of borrowing facilities in the banking system Level 2 Lack of information transparency of publishers Lack of financial engineering and innovation in risk hedging tools Lack of specialized activity of insurance institutions in the debt market Level 3 Lack of access to the international money market The intervention of supervisory bodies in the pricing of all bonds at the same rate The lower financing rate in the banking system compared to debt securities Neglecting the relationship between risk and expected return Lack of development of credit rating companies Level 4 Lack of government guarantees to cover risk Mandatory limit on the interest rate of financial instruments Instability and turbulence in the macroeconomics and the unpredictability of some key indicators such as the inflation rate Level 5 Interest rate control by supervisory institutions International sanctions of the country Level 6 The existence of the government as an active member in the debt market Level 7 Figure 1. The leveling of the components Discussion and conclusions The purpose of this study was to analyze the country's financing system damages with an emphasis on the debt market, and to achieve this, a combined quantitative and qualitative method was used. At first, using the qualitative method of semi-structured interviews with selected experts, the factors of the lack of development of the debt market in the country were identified, and then the relationships between the factors were identified through the ISM method, and the identified factors were classified using the MICMAC analysis. Twenty-four factors were identified as obstacles to the development of the debt market, and the highest-level factors were interest rate control by supervisory institutions, the existence of the government as an active performer in the debt market, and international sanctions of the country. In this study, in comparison with previous studies, the cause and effect leveling of the obstacles to the development of the debt market in Iran was done, on the basis of which it can be suggested that the supervisory institutions should first prevent the influence of the government as the issuer of bonds on the policies of bond issuance. Then, by actualizing the interest rate, they set the interest rate freely so that each publisher can finance debt in the market based on their own risk. Finally, for future research, it is suggested to deal with how to fix the damages identified in this study. In addition, it is suggested to conduct studies on the effectiveness of inflation-based tools to fix the real interest rate in Iran's economy and examine the component of determining the mandated interest rate in the economy. An efficient financial system leads to the aggregation and optimal allocation of resources in the economy. In advanced financial markets such as the United States, the economy is mainly financed through debt securities and stocks. This is despite the fact that in Iran, a small part of financing is done through debt securities and shares, and most of it is done through the banking system. The purpose of this study is to analyze the financing system of the country with an emphasis on the debt market. To achieve the above goal, a combined quantitative and qualitative method was used. At first, using the qualitative method of semi-structured interviews with selected experts, the factors of the lack of development of the debt market in the country were identified, and then the relationships between the factors were identified through the ISM method, and the identified factors were classified using MICMAC analysis. In this research, 24 factors were identified as obstacles to the development of the debt market, the highest-level factors were interest rate control by supervisory institutions, the existence of the government as an active member in the debt market, and international sanctions of the country. Keywords: Debt Market, Production Financing, Islamic Securities, Interpretive Structural Modeling (ISM). Introduction Sustainable growth and development is the main economic ideal in any country. The most important criterion for determining and measuring economic growth, GDP growth, and the main driver of production is the amount of investment. Several factors including monetary and financial policies and different methods of financing can affect the amount and volume of investments. An efficient financial system leads to the aggregation and optimal allocation of resources in the economy. Considering the role of the financial system in the functioning and economic growth, studies in the field of financial development have found a special place in the economic literature. As the economy is in the early stages of development, due to the lack of proper infrastructure for market activity, the financial system tends to be bank-oriented. In Iran, banks are mainly responsible for financing, and the capital market has not played a significant role in this process. The deeper and more developed the financial market is the companies, and investors will be able to choose the optimal ratio of debt to finance their investment projects, and this will lead to the expansion of economic opportunities, income distribution, and the reduction of income inequalities. Finally, it leads to economic growth in the society. In most advanced financing systems, the major part of financing is done via debt instruments that are issued through the capital market. Therefore, the purpose of this study is to analyze the damages of the financing system of the country in the sector of debt market development. Materials and Methods In this study, to achieve the goals, in the first stage, a literature review and identification of patterns and dimensions were done. In the next step, using a semi-structured interview, the best criteria and their relationship were identified in each dimension. In the last stage of the research model, the relationship model between the factors was identified using the interpretive structural modeling (ISM) technique. Findings By reviewing the literature on the identified factors and by using the in-depth interview technique, the identified factors were completed. Finally, by using the structural interpretive modeling technique, the relationship between patterns was determined. The factors and their leveling are shown in Figure 1: Mismatch between inflation and bond yields Absence of active marketer in the published securities in the market Easier financing through the banking system than issuing debt securities Low depth of the secondary market of debt securities The complexity and time-consuming process of security issuance in Iran's debt market Level 1 Low savings ratio in the country's economy Investors' lack of confidence in strict monitoring of issuers Lack of culture for the issuance of debt securities among issuers Lack of practical financial knowledge among publishers' managers The possibility of borrowing facilities in the banking system Level 2 Lack of information transparency of publishers Lack of financial engineering and innovation in risk hedging tools Lack of specialized activity of insurance institutions in the debt market Level 3 Lack of access to the international money market The intervention of supervisory bodies in the pricing of all bonds at the same rate The lower financing rate in the banking system compared to debt securities Neglecting the relationship between risk and expected return Lack of development of credit rating companies Level 4 Lack of government guarantees to cover risk Mandatory limit on the interest rate of financial instruments Instability and turbulence in the macroeconomics and the unpredictability of some key indicators such as the inflation rate Level 5 Interest rate control by supervisory institutions International sanctions of the country Level 6 The existence of the government as an active member in the debt market Level 7 Figure 1. The leveling of the components Discussion and conclusions The purpose of this study was to analyze the country's financing system damages with an emphasis on the debt market, and to achieve this, a combined quantitative and qualitative method was used. At first, using the qualitative method of semi-structured interviews with selected experts, the factors of the lack of development of the debt market in the country were identified, and then the relationships between the factors were identified through the ISM method, and the identified factors were classified using the MICMAC analysis. Twenty-four factors were identified as obstacles to the development of the debt market, and the highest-level factors were interest rate control by supervisory institutions, the existence of the government as an active performer in the debt market, and international sanctions of the country. In this study, in comparison with previous studies, the cause and effect leveling of the obstacles to the development of the debt market in Iran was done, on the basis of which it can be suggested that the supervisory institutions should first prevent the influence of the government as the issuer of bonds on the policies of bond issuance. Then, by actualizing the interest rate, they set the interest rate freely so that each publisher can finance debt in the market based on their own risk. Finally, for future research, it is suggested to deal with how to fix the damages identified in this study. In addition, it is suggested to conduct studies on the effectiveness of inflation-based tools to fix the real interest rate in Iran's economy and examine the component of determining the mandated interest rate in the economy.

https://amf.ui.ac.ir/article_28314_2c2704f25cdd5d79ab49edbc79058e9f.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 11 4 2023 12 22 Measuring the Volatility Persistence of the Tehran Stock Exchange using Stochastic Volatility Models with Jump in Return Measuring the Volatility Persistence of the Tehran Stock Exchange using Stochastic Volatility Models with Jump in Return 121 140 28390 10.22108/amf.2024.139957.1841 FA Mohammad Mehdi Momenzadeh Ph.D. Candidate in Accounting, Department of Accounting, Faculty of Management, University of Tehran, Tehran, Iran Moslem Nilchi Ph.D., Department of Finance and Accounting, Faculty of Economics, Management and Accounting, University of Yazd, Yazd, Iran Mojtaba Rostami Ph.D., Department of Economics, Faculty of Economics, Management and Accounting, University of Yazd, Yazd, Iran Journal Article 2023 12 12 The long-term behavior of stock markets is of considerable importance to asset managers and financial experts due to its direct relationship with stock price valuation. Volatility persistence has a significant effect on stock price returns. Therefore, the relationship between heavy falls in the stock market can be related to the phenomenon of high volatility persistence. In the present study, using the Bayesian unit root test, the persistence of the Tehran stock Exchange volatility has been investigated in the framework of SV and SVJ. The results of this test using the Bayesian factor in different specifications of both SV and SVJ models show that although the unit root is rejected in the volatility of the Tehran stock Exchange prices in the period of 1398-1400 (2019-2021), the persistence of the volatility was very high. The increase of irrational traders in this period of time has been one of the reasons for increasing the persistence of market volatility. The findings show that the flow of information in the Tehran stock market and the absorption of information in its prices are slow. The characteristic of high volatility persistence in this market is the result of its closed structure and the concentration of market weight on a few main groups. As a result, regular release of financial statements, training of traders, and use of expert analysis along with diversification of investors and not focusing on specific pledges in portfolio formation will help to reduce volatility persistence. Keywords: Stock Prices, Volatility Persistence, Stochastic Volatility, Volatility Unit Root. Introduction In financial literature, stock market performance depends on the relationship between stock market price volatility and changes in stock price levels (Bekaert et al., 2009). Pindyck (1981) considers the poor performance of the New York stock market in 1970 to be due to the increase in the volatility of this market, which increased risk. Mandimika and Chinzara (2012) state that what matters in determining the relationship between volatility and stock prices is volatility persistence. Because only volatility persistence justifies changes in risk allocation. In the definition, volatility persistence refers to the resistance of volatility to return to its long-term average level and shows the duration of persistence of Volatility shocks (Wang & Yang, 2017). Therefore, investigating the question of whether stock market volatility shocks contain long-term or temporary effects is very important (Nilchi et al, 2022). In this regard, this study used two models of stochastic volatility (SV) and stochastic Volatility with the jump in return (SVJ). This study is important for investment practitioners and market participants because it examines the extent of volatility persistence in the current environment, which has important implications for risk management and portfolio management. Materials and Methods Using a mathematical model, Poterba and Summers (1984) investigated the relationship between changes in stock prices and changes in volatility persistence. This study investigates the model presented by Poterba and Summers (1984) regarding the type of econometric methods, and unit root tests in volatility using SV and SVJ models. Due to the discrete form of the maximum likelihood function of these models, their parameters are estimated using the Bayesian method. Andersen et al. (1999) consider this method to be more efficient than other alternative methods and the reason for this is the use of the Monte Carlo simulation of Markov chains or MCMC. Research Findings Both SV and SVJ models indicated that volatility persistence is high in all three time frames of this research. This feature can happen due to the closed structure of the Tehran stock Exchange, as a result of which the flow of information in the market and the absorption of information in stock prices are slow. On the other hand, the concentration of the market weight on a few main groups can be another factor of high volatility persistence in the Tehran stock Exchange. However, from 2019 to 2021, the degree of volatility persistence was slightly higher than in other periods, and one of the most important reasons for this is the increase in the number of irrational traders in this period. Irrational traders have the potential to enhance volatility persistence. It is worth noting that the volatility persistence in the Tehran stock Exchange cannot be attributed to changes that are periodic (such as profit disclosure and seasonal changes in the supply and demand of certain market groups). On the other hand, the results of comparing the estimates of SV and SVJ models show that the existence of a jump component for modeling volatility in the daily return of the total stock index will have a strong impact on the calculation of volatility persistence in this market, and therefore, ignoring this component in the models can lead to inaccurate results. Discussion of Results and Conclusions Paying attention to this results is necessary for designing risk-hedging strategies and forecasting future market performance. In another part of this study, the unit root test in volatility showed that despite the high volatility persistence, the hypothesis of the existence of a unit root of volatility is rejected. Therefore, the hypothesis of long-term market collapse as a result of extreme volatility persistence (ϕ=1) is not confirmed using the SVJ model. The consequence of this issue is that the volatility waves caused by negative shocks on the capital market will not have an extreme volatility persistence and the long-term activity of investors in this market does not face a serious risk (heavy falls), and on the other hand, this means that the market immediately reacts to the information that the financial system does not react to it, but gradually reacts to it over time. The long-term behavior of stock markets is of considerable importance to asset managers and financial experts due to its direct relationship with stock price valuation. Volatility persistence has a significant effect on stock price returns. Therefore, the relationship between heavy falls in the stock market can be related to the phenomenon of high volatility persistence. In the present study, using the Bayesian unit root test, the persistence of the Tehran stock Exchange volatility has been investigated in the framework of SV and SVJ. The results of this test using the Bayesian factor in different specifications of both SV and SVJ models show that although the unit root is rejected in the volatility of the Tehran stock Exchange prices in the period of 1398-1400 (2019-2021), the persistence of the volatility was very high. The increase of irrational traders in this period of time has been one of the reasons for increasing the persistence of market volatility. The findings show that the flow of information in the Tehran stock market and the absorption of information in its prices are slow. The characteristic of high volatility persistence in this market is the result of its closed structure and the concentration of market weight on a few main groups. As a result, regular release of financial statements, training of traders, and use of expert analysis along with diversification of investors and not focusing on specific pledges in portfolio formation will help to reduce volatility persistence. Keywords: Stock Prices, Volatility Persistence, Stochastic Volatility, Volatility Unit Root. Introduction In financial literature, stock market performance depends on the relationship between stock market price volatility and changes in stock price levels (Bekaert et al., 2009). Pindyck (1981) considers the poor performance of the New York stock market in 1970 to be due to the increase in the volatility of this market, which increased risk. Mandimika and Chinzara (2012) state that what matters in determining the relationship between volatility and stock prices is volatility persistence. Because only volatility persistence justifies changes in risk allocation. In the definition, volatility persistence refers to the resistance of volatility to return to its long-term average level and shows the duration of persistence of Volatility shocks (Wang & Yang, 2017). Therefore, investigating the question of whether stock market volatility shocks contain long-term or temporary effects is very important (Nilchi et al, 2022). In this regard, this study used two models of stochastic volatility (SV) and stochastic Volatility with the jump in return (SVJ). This study is important for investment practitioners and market participants because it examines the extent of volatility persistence in the current environment, which has important implications for risk management and portfolio management. Materials and Methods Using a mathematical model, Poterba and Summers (1984) investigated the relationship between changes in stock prices and changes in volatility persistence. This study investigates the model presented by Poterba and Summers (1984) regarding the type of econometric methods, and unit root tests in volatility using SV and SVJ models. Due to the discrete form of the maximum likelihood function of these models, their parameters are estimated using the Bayesian method. Andersen et al. (1999) consider this method to be more efficient than other alternative methods and the reason for this is the use of the Monte Carlo simulation of Markov chains or MCMC. Research Findings Both SV and SVJ models indicated that volatility persistence is high in all three time frames of this research. This feature can happen due to the closed structure of the Tehran stock Exchange, as a result of which the flow of information in the market and the absorption of information in stock prices are slow. On the other hand, the concentration of the market weight on a few main groups can be another factor of high volatility persistence in the Tehran stock Exchange. However, from 2019 to 2021, the degree of volatility persistence was slightly higher than in other periods, and one of the most important reasons for this is the increase in the number of irrational traders in this period. Irrational traders have the potential to enhance volatility persistence. It is worth noting that the volatility persistence in the Tehran stock Exchange cannot be attributed to changes that are periodic (such as profit disclosure and seasonal changes in the supply and demand of certain market groups). On the other hand, the results of comparing the estimates of SV and SVJ models show that the existence of a jump component for modeling volatility in the daily return of the total stock index will have a strong impact on the calculation of volatility persistence in this market, and therefore, ignoring this component in the models can lead to inaccurate results. Discussion of Results and Conclusions Paying attention to this results is necessary for designing risk-hedging strategies and forecasting future market performance. In another part of this study, the unit root test in volatility showed that despite the high volatility persistence, the hypothesis of the existence of a unit root of volatility is rejected. Therefore, the hypothesis of long-term market collapse as a result of extreme volatility persistence (ϕ=1) is not confirmed using the SVJ model. The consequence of this issue is that the volatility waves caused by negative shocks on the capital market will not have an extreme volatility persistence and the long-term activity of investors in this market does not face a serious risk (heavy falls), and on the other hand, this means that the market immediately reacts to the information that the financial system does not react to it, but gradually reacts to it over time.

https://amf.ui.ac.ir/article_28390_720fc8ddcd4f541108d9b726fd9f8ea3.pdf