Shahid Beheshti UniversityFinancial Management Perspective2645-4637144620240822Concern About Information Spillover and Choosing AuditorConcern About Information Spillover and Choosing Auditor93110489410.48308/jfmp.2024.104894FAMozaffarJamalianpourAssistant Professor, Department of Accounting, Allameh Tabataba'i University, Tehran, Iran0000-0002-2649-5975SinaAsnaashariPh.D. Candidate, Department of Accounting, Shahid Beheshti University, Tehran, Iran0009-0007-9943-4704Journal Article20240311<strong>Purpose:</strong> Auditors, due to their informational oversight of client activities, gain extensive knowledge about companies' operations. Much of this information carries competitive advantages, necessitating robust measures to ensure client confidentiality. However, auditors may inadvertently facilitate the dissemination of a client’s proprietary information to others, a phenomenon referred to as information spillover. This spillover, especially involving competitive information, raises significant concerns for companies. Consequently, firms may avoid selecting the same auditor as their competitors within the same industry to prevent the leakage of sensitive information. This study examines the impact of companies' concerns about information spillover on their auditor selection decisions.
<strong>Method:</strong> To determine whether concerns about information spillover affect the choice of the same auditor among companies within the same industry, this study analyzed data from ten industries listed on the Tehran Stock Exchange over a five-year period (2017–2021). These industries include five innovative sectors ("Telecommunications," "Information and Communication," "Computers and Related Activities," "Electrical Machinery," and "Pharmaceuticals") and five non-innovative sectors ("Transportation and Warehousing," "Tiles and Ceramics," "Metal Products," "Chemicals," and "Cement, Plaster, and Lime"). Since information spillover concerns are justified only among companies operating in the same industry, all possible pairs of companies within each sector were examined. Proxies for measuring spillover concerns, such as research and development (R&D) costs, innovation, new product introduction, and increases in intangible assets, were defined. A logistic regression model was employed to assess the impact of these proxies on auditor selection. Additionally, a composite "concern intensity" index was created by aggregating the individual proxies to provide a conclusive measure of spillover concerns and their effect on auditor choice.
<strong>Findings:</strong> The logistic regression results indicate that companies operating in innovative industries avoid selecting the same auditor as their industry peers. This significant negative relationship between information spillover concerns and the choice of the same auditor was also evident in proxies such as new product introduction and increases in intangible assets. However, spillover concerns measured by R&D costs did not significantly influence auditor selection within the same industry. The composite "concern intensity" index showed a significant negative impact on the likelihood of selecting the same auditor, confirming that firms concerned about information spillover are less likely to choose the same auditing firm as other companies in their industry.
<strong>Conclusion:</strong> The findings reveal that companies concerned about information spillover, especially those operating in innovative industries, introducing new products, or with increased intangible assets, tend to avoid selecting the same auditor as their industry peers. However, concerns related to R&D costs did not exhibit a significant impact on this decision. As the aggregated concern intensity index demonstrates a strong negative effect on auditor selection, information spillover concerns emerge as a significant determinant of auditor choice. The results underscore the importance of enhancing professional auditing standards and ethical guidelines to improve client confidentiality practices, thereby mitigating the influence of spillover concerns on auditor selection.<strong>Purpose:</strong> Auditors, due to their informational oversight of client activities, gain extensive knowledge about companies' operations. Much of this information carries competitive advantages, necessitating robust measures to ensure client confidentiality. However, auditors may inadvertently facilitate the dissemination of a client’s proprietary information to others, a phenomenon referred to as information spillover. This spillover, especially involving competitive information, raises significant concerns for companies. Consequently, firms may avoid selecting the same auditor as their competitors within the same industry to prevent the leakage of sensitive information. This study examines the impact of companies' concerns about information spillover on their auditor selection decisions.
<strong>Method:</strong> To determine whether concerns about information spillover affect the choice of the same auditor among companies within the same industry, this study analyzed data from ten industries listed on the Tehran Stock Exchange over a five-year period (2017–2021). These industries include five innovative sectors ("Telecommunications," "Information and Communication," "Computers and Related Activities," "Electrical Machinery," and "Pharmaceuticals") and five non-innovative sectors ("Transportation and Warehousing," "Tiles and Ceramics," "Metal Products," "Chemicals," and "Cement, Plaster, and Lime"). Since information spillover concerns are justified only among companies operating in the same industry, all possible pairs of companies within each sector were examined. Proxies for measuring spillover concerns, such as research and development (R&D) costs, innovation, new product introduction, and increases in intangible assets, were defined. A logistic regression model was employed to assess the impact of these proxies on auditor selection. Additionally, a composite "concern intensity" index was created by aggregating the individual proxies to provide a conclusive measure of spillover concerns and their effect on auditor choice.
<strong>Findings:</strong> The logistic regression results indicate that companies operating in innovative industries avoid selecting the same auditor as their industry peers. This significant negative relationship between information spillover concerns and the choice of the same auditor was also evident in proxies such as new product introduction and increases in intangible assets. However, spillover concerns measured by R&D costs did not significantly influence auditor selection within the same industry. The composite "concern intensity" index showed a significant negative impact on the likelihood of selecting the same auditor, confirming that firms concerned about information spillover are less likely to choose the same auditing firm as other companies in their industry.
<strong>Conclusion:</strong> The findings reveal that companies concerned about information spillover, especially those operating in innovative industries, introducing new products, or with increased intangible assets, tend to avoid selecting the same auditor as their industry peers. However, concerns related to R&D costs did not exhibit a significant impact on this decision. As the aggregated concern intensity index demonstrates a strong negative effect on auditor selection, information spillover concerns emerge as a significant determinant of auditor choice. The results underscore the importance of enhancing professional auditing standards and ethical guidelines to improve client confidentiality practices, thereby mitigating the influence of spillover concerns on auditor selection.https://jfmp.sbu.ac.ir/article_104894_25a48c5eb376d96123f15dbdf67eaa39.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637144620240822The role of market entry time in strengthening or weakening the investors' Disposition EffectThe role of market entry time in strengthening or weakening the investors' Disposition Effect335810490410.48308/jfmp.2024.104904FARezaEyvazlooAssistant Professor, Department of Finance, College of Management, University of Tehran, Tehran, Iran0000-0002-9336-078XRezaRaeiProfessor, Department of Finance, College of Management, University of Tehran, Tehran, Iran.0000-0003-4865-5316FarzadRezaeiMSc. in Finance, College of Management, University of Tehran, Tehran, Iran.0009-0000-1040-2773Journal Article20240701<strong>Purpose:</strong> This study investigates the existence and intensity of the Disposition Effect among individual investors. The Disposition Effect refers to investors’ tendency to sell assets with positive returns while holding onto those with negative returns. This behavioral bias can lead to asset mispricing and reduced market efficiency as decisions are influenced by cognitive errors, psychological factors, and emotional responses. The research focuses on whether the timing of market entry impacts the strength of the Disposition Effect, particularly as market entry is influenced by broader economic and psychological conditions. Understanding these effects can enhance financial decision-making and investment strategies, thereby contributing to improved market performance and stability across diverse conditions.
<strong>Method:</strong> The statistical population includes all individual shareholders in the Iranian capital market whose trading data were recorded from early July to early December 2022. To achieve the research objectives, trading data were collected, including transaction dates, directions, prices, and volumes, alongside personal information such as age, gender, trading history, and account balances. These data were analyzed to assess the presence and intensity of the Disposition Effect. Investor market entry data were categorized into sub-samples based on monthly market returns, volatility, and economic instability indicators. Statistical methods, including survival analysis and the Kaplan-Meier model, were employed. Survival analysis assessed how long investors remain influenced by the Disposition Effect under varying market conditions, while the Kaplan-Meier model analyzed the distribution of the Disposition Effect’s duration and examined how both market entry timing and prevailing market conditions influence its intensity.
<strong>Findings:</strong> The findings indicate that the Disposition Effect is particularly pronounced among new investors, especially during unfavorable market conditions. These conditions include periods with lower overall market returns, higher market volatility, and heightened economic uncertainty. New investors exhibited a stronger tendency to hold onto poorly performing stocks and sell stocks with positive returns, reflecting the psychological and cognitive challenges they face during the initial stages of their market involvement. In contrast, investors with greater trading experience demonstrated a reduced tendency toward the Disposition Effect, highlighting the mitigating influence of experience over time. These results underscore the significant role of market conditions and entry timing in shaping investment behavior and behavioral biases across varying investor profiles.
<strong>Conclusion:</strong> The results emphasize the importance of understanding the Disposition Effect and its relationship with market entry timing. New investors, particularly those entering the market during adverse conditions, are more prone to exhibiting the Disposition Effect, often making suboptimal decisions. These insights can guide financial managers and investment advisors in developing tailored strategies, such as investor education and advisory services, to address behavioral biases and enhance decision-making processes. Additionally, the findings can assist policymakers in designing targeted interventions to mitigate the negative effects of behavioral biases, thereby fostering a more efficient and resilient market environment. Improved awareness and better management of the Disposition Effect can contribute to better investment practices, reduce risks, and enhance overall market stability.<strong>Purpose:</strong> This study investigates the existence and intensity of the Disposition Effect among individual investors. The Disposition Effect refers to investors’ tendency to sell assets with positive returns while holding onto those with negative returns. This behavioral bias can lead to asset mispricing and reduced market efficiency as decisions are influenced by cognitive errors, psychological factors, and emotional responses. The research focuses on whether the timing of market entry impacts the strength of the Disposition Effect, particularly as market entry is influenced by broader economic and psychological conditions. Understanding these effects can enhance financial decision-making and investment strategies, thereby contributing to improved market performance and stability across diverse conditions.
<strong>Method:</strong> The statistical population includes all individual shareholders in the Iranian capital market whose trading data were recorded from early July to early December 2022. To achieve the research objectives, trading data were collected, including transaction dates, directions, prices, and volumes, alongside personal information such as age, gender, trading history, and account balances. These data were analyzed to assess the presence and intensity of the Disposition Effect. Investor market entry data were categorized into sub-samples based on monthly market returns, volatility, and economic instability indicators. Statistical methods, including survival analysis and the Kaplan-Meier model, were employed. Survival analysis assessed how long investors remain influenced by the Disposition Effect under varying market conditions, while the Kaplan-Meier model analyzed the distribution of the Disposition Effect’s duration and examined how both market entry timing and prevailing market conditions influence its intensity.
<strong>Findings:</strong> The findings indicate that the Disposition Effect is particularly pronounced among new investors, especially during unfavorable market conditions. These conditions include periods with lower overall market returns, higher market volatility, and heightened economic uncertainty. New investors exhibited a stronger tendency to hold onto poorly performing stocks and sell stocks with positive returns, reflecting the psychological and cognitive challenges they face during the initial stages of their market involvement. In contrast, investors with greater trading experience demonstrated a reduced tendency toward the Disposition Effect, highlighting the mitigating influence of experience over time. These results underscore the significant role of market conditions and entry timing in shaping investment behavior and behavioral biases across varying investor profiles.
<strong>Conclusion:</strong> The results emphasize the importance of understanding the Disposition Effect and its relationship with market entry timing. New investors, particularly those entering the market during adverse conditions, are more prone to exhibiting the Disposition Effect, often making suboptimal decisions. These insights can guide financial managers and investment advisors in developing tailored strategies, such as investor education and advisory services, to address behavioral biases and enhance decision-making processes. Additionally, the findings can assist policymakers in designing targeted interventions to mitigate the negative effects of behavioral biases, thereby fostering a more efficient and resilient market environment. Improved awareness and better management of the Disposition Effect can contribute to better investment practices, reduce risks, and enhance overall market stability.https://jfmp.sbu.ac.ir/article_104904_a8736b2add656fbb60dcc45de1697020.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637144620240822Identifying and Evaluating the Effective Fields of Metaverse in Financial Flexibility: Future Perspectives of the Capital MarketIdentifying and Evaluating the Effective Fields of Metaverse in Financial Flexibility: Future Perspectives of the Capital Market599110490510.48308/jfmp.2024.104905FASamantaGholjashPh.D. Candidate, Department of Accounting, Aliabad katoul Branch, Islamic Azad University, Aliabad katoul, Iran.AbolfazlMomeni YanesariAssistant professor of Accounting, Department of Administrative and Economics, Gonbad Kavous University, Gonbad, Iran0000-0003-2465-292XLeilaAjamAssistant Professor, Department of Computer, Aliabad katoul Branch, Islamic Azad University, Aliabad katoul, Iran.MehdiSafari GrayliAssociate Professor, Department of Accounting, Bandar Gaz Branch, Islamic Azad University, Bandar Gaz, Iran.Journal Article20240701<strong>Purpose:</strong> Over the past decade, there has been increasing attention to business interactions in parallel worlds based on virtual spaces, particularly with the rising acceptance of such transformations during the COVID-19 pandemic (Gupta et al., 2024). These changes have significantly impacted various fields, including corporate financial management, as organizations like Facebook, Coca-Cola, and Disney have integrated Metaverse applications into their financial decision-making processes (Kumar et al., 2023). For example, in July 2021, Facebook announced an investment of at least $10 billion over the next five years to develop operations in the Metaverse. The company even rebranded itself as Meta to reduce financial costs and establish itself as a leading competitor in the emerging field (Kraus et al., 2022).
<strong>Method:</strong> The nature of any research in the humanities can be categorized based on its results, objectives, and data type. Accordingly, this study is developmental in nature as it examines a phenomenon that lacks sufficient theoretical coherence to serve as a measurement tool, as identified through a review of previous research. The qualitative section of this study aims to identify the effective dimensions of the Metaverse in financial flexibility. From an objective standpoint, this study is exploratory, as the expansion of Metaverse functions in financial management is an emerging phenomenon. Using grounded theory, the study seeks to present the dimensions of this concept in a multidimensional model. From a data-type perspective, this research adopts a mixed-methods approach. In the qualitative phase, data collected through interviews underwent three stages of coding—open, axial, and selective—to identify the factors influencing the Metaverse at the level of capital market companies. In the quantitative phase, scenario-based analysis was conducted using the "row i-column j" matrix and Scenario Wizard software. This phase defined potential scenarios related to the study context and expanded possible outcomes through mathematical function matrices.
<strong>Findings:</strong> Given the absence of a coherent theoretical framework for implementing the Metaverse to enhance the financial flexibility of capital market companies, grounded theory analysis was employed in the first phase. Through 12 interviews and three stages of coding, three main categories, six core components, and 35 conceptual themes were identified. A Delphi analysis confirmed the reliability of these components. To develop future scenarios for implementing financial Metaverses, a linkage matrix was used to identify the most influential key components by determining the inputs and outputs of the matrix model through the MicMac matrix. The results revealed two primary dimensions: the strategic capacities of the Metaverse and its systematic implementation. These dimensions serve as the foundation for evaluating scenarios of financial flexibility based on the Metaverse. The reciprocal matrix identified the scenarios that best describe the phenomenon under investigation.
<strong>Conclusion:</strong> The study aimed to identify and evaluate the effective dimensions of the Metaverse in financial flexibility, focusing on future perspectives of the capital market. The results indicate that the most favorable scenario, termed "Meta-Jurassic," represents a balanced integration of the Metaverse’s strategic capacities and systematic implementation. According to this scenario, capital market companies require structural insights when adopting Metaverse technologies aligned with their operational nature to achieve reliable and sustainable financial flexibility. Given the nascent state of this technology in developing economies like Iran, rapid adoption without sufficient technological and strategic infrastructure is not advisable. Instead, companies should align their strategic and systemic capacities with the Metaverse’s functionalities to create competitive advantages and enhance financial flexibility.<strong>Purpose:</strong> Over the past decade, there has been increasing attention to business interactions in parallel worlds based on virtual spaces, particularly with the rising acceptance of such transformations during the COVID-19 pandemic (Gupta et al., 2024). These changes have significantly impacted various fields, including corporate financial management, as organizations like Facebook, Coca-Cola, and Disney have integrated Metaverse applications into their financial decision-making processes (Kumar et al., 2023). For example, in July 2021, Facebook announced an investment of at least $10 billion over the next five years to develop operations in the Metaverse. The company even rebranded itself as Meta to reduce financial costs and establish itself as a leading competitor in the emerging field (Kraus et al., 2022).
<strong>Method:</strong> The nature of any research in the humanities can be categorized based on its results, objectives, and data type. Accordingly, this study is developmental in nature as it examines a phenomenon that lacks sufficient theoretical coherence to serve as a measurement tool, as identified through a review of previous research. The qualitative section of this study aims to identify the effective dimensions of the Metaverse in financial flexibility. From an objective standpoint, this study is exploratory, as the expansion of Metaverse functions in financial management is an emerging phenomenon. Using grounded theory, the study seeks to present the dimensions of this concept in a multidimensional model. From a data-type perspective, this research adopts a mixed-methods approach. In the qualitative phase, data collected through interviews underwent three stages of coding—open, axial, and selective—to identify the factors influencing the Metaverse at the level of capital market companies. In the quantitative phase, scenario-based analysis was conducted using the "row i-column j" matrix and Scenario Wizard software. This phase defined potential scenarios related to the study context and expanded possible outcomes through mathematical function matrices.
<strong>Findings:</strong> Given the absence of a coherent theoretical framework for implementing the Metaverse to enhance the financial flexibility of capital market companies, grounded theory analysis was employed in the first phase. Through 12 interviews and three stages of coding, three main categories, six core components, and 35 conceptual themes were identified. A Delphi analysis confirmed the reliability of these components. To develop future scenarios for implementing financial Metaverses, a linkage matrix was used to identify the most influential key components by determining the inputs and outputs of the matrix model through the MicMac matrix. The results revealed two primary dimensions: the strategic capacities of the Metaverse and its systematic implementation. These dimensions serve as the foundation for evaluating scenarios of financial flexibility based on the Metaverse. The reciprocal matrix identified the scenarios that best describe the phenomenon under investigation.
<strong>Conclusion:</strong> The study aimed to identify and evaluate the effective dimensions of the Metaverse in financial flexibility, focusing on future perspectives of the capital market. The results indicate that the most favorable scenario, termed "Meta-Jurassic," represents a balanced integration of the Metaverse’s strategic capacities and systematic implementation. According to this scenario, capital market companies require structural insights when adopting Metaverse technologies aligned with their operational nature to achieve reliable and sustainable financial flexibility. Given the nascent state of this technology in developing economies like Iran, rapid adoption without sufficient technological and strategic infrastructure is not advisable. Instead, companies should align their strategic and systemic capacities with the Metaverse’s functionalities to create competitive advantages and enhance financial flexibility.https://jfmp.sbu.ac.ir/article_104905_11a1b877e1a3a62087a075c2fc39dc2c.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637144620240822The Contexts Genealogy of the Toxic Assets Emergence in Capital Market Companies: The Application of a Paradigmatic FrameworkThe Contexts Genealogy of the Toxic Assets Emergence in Capital Market Companies: The Application of a Paradigmatic Framework9313110502910.48308/jfmp.2024.105029FABehroozShirkhaniPh.D. Candidate, Department of Accounting, Shahrood Branch, Islamic Azad University, Shahrood, IranMohammadrezaAbdoliAssociate Professor, Department of Accounting, Shahrood Branch, Islamic Azad University, Shahroud, Iran0000-0002-8927-1231HasanValiyanAssistant Professor, Department of Accounting, Shahrood Branch, Islamic Azad University, Shahroud, Iran0000-0002-3365-1593MaryamShahriAssistant Professor, Department of Accounting, Shahrood Branch, Islamic Azad University, Shahroud, Iran0009-0005-6160-1672Journal Article20240917<strong>Purpose:</strong> Financial crises have long been associated with the emergence of toxic assets in commercial companies that are significantly active in capital markets. Toxic assets, by definition, are those whose market value is lower than their nominal or recorded value in financial statements due to their lack of trading appeal. These assets are often challenging to evaluate and remain concealed within the complex financial operations of companies. Over time, the accumulation of such assets poses serious risks to capital market companies, exposing them to potential stock price crashes. Given the importance of understanding the underlying causes of toxic asset accumulation, this study aims to investigate the genealogy of the contexts in which toxic assets emerge in capital market companies, utilizing a paradigmatic framework for analysis.
<strong>Method:</strong> This study employs a multi-step approach, beginning with a series of expert interviews to identify the underlying nature of toxic assets. The questions posed during the interviews were carefully adjusted based on the specific conditions of each session and the insights provided by the participants. This iterative adjustment ensured that the interviews remained focused on the core phenomenon under investigation. A detailed protocol outlining the study's primary themes was developed to guide the interviews, facilitating the extraction of open codes. These open codes were then analyzed and refined based on patterns of repetition and conceptual similarity. The refined codes were transformed into propositional themes, enabling a deeper understanding of the phenomenon under investigation. To apply the paradigmatic framework as a methodological foundation, the study involved empiricists who participated in this analytical process. Subsequently, a focal group was established to evaluate the identified propositions. Using a hierarchical checklist, each statement was systematically categorized into specific types, aligning with the semantic structure of the paradigmatic model. This rigorous approach ensured that the findings were grounded in a structured analytical framework.
<strong>Findings:</strong> In the qualitative phase, after eliminating redundancies and similar open codes, 51 contextual propositions were identified. These propositions were cross-referenced with related research to validate their relevance and ensure their applicability to the subsequent stages of the study. In the quantitative phase, the paradigmatic framework categorized the 51 contextual propositions into five distinct domains: causal conditions, contextual factors, intervening conditions, strategies, and consequences. This categorization provided a comprehensive view of the factors contributing to the emergence of toxic assets.
<strong>Conclusion:</strong> The results highlight that toxic asset emergence is influenced by a wide range of factors, from managerial functions to broader economic and structural aspects. While strategies such as information concealment or the positive persuasion of stakeholders’ informational needs may temporarily mitigate the negative effects of toxic assets, their prolonged accumulation often results in a cascade of negative consequences. Companies face increased risks of stock price crashes due to delayed information dissemination and the subsequent surge of negative news. Additionally, delays in selling these assets, even at prices below their nominal value, exacerbate financial losses due to the lack of a suitable trading market. These findings underscore the critical need for proactive strategies to address toxic asset accumulation, thereby minimizing risks and safeguarding financial stability.<strong>Purpose:</strong> Financial crises have long been associated with the emergence of toxic assets in commercial companies that are significantly active in capital markets. Toxic assets, by definition, are those whose market value is lower than their nominal or recorded value in financial statements due to their lack of trading appeal. These assets are often challenging to evaluate and remain concealed within the complex financial operations of companies. Over time, the accumulation of such assets poses serious risks to capital market companies, exposing them to potential stock price crashes. Given the importance of understanding the underlying causes of toxic asset accumulation, this study aims to investigate the genealogy of the contexts in which toxic assets emerge in capital market companies, utilizing a paradigmatic framework for analysis.
<strong>Method:</strong> This study employs a multi-step approach, beginning with a series of expert interviews to identify the underlying nature of toxic assets. The questions posed during the interviews were carefully adjusted based on the specific conditions of each session and the insights provided by the participants. This iterative adjustment ensured that the interviews remained focused on the core phenomenon under investigation. A detailed protocol outlining the study's primary themes was developed to guide the interviews, facilitating the extraction of open codes. These open codes were then analyzed and refined based on patterns of repetition and conceptual similarity. The refined codes were transformed into propositional themes, enabling a deeper understanding of the phenomenon under investigation. To apply the paradigmatic framework as a methodological foundation, the study involved empiricists who participated in this analytical process. Subsequently, a focal group was established to evaluate the identified propositions. Using a hierarchical checklist, each statement was systematically categorized into specific types, aligning with the semantic structure of the paradigmatic model. This rigorous approach ensured that the findings were grounded in a structured analytical framework.
<strong>Findings:</strong> In the qualitative phase, after eliminating redundancies and similar open codes, 51 contextual propositions were identified. These propositions were cross-referenced with related research to validate their relevance and ensure their applicability to the subsequent stages of the study. In the quantitative phase, the paradigmatic framework categorized the 51 contextual propositions into five distinct domains: causal conditions, contextual factors, intervening conditions, strategies, and consequences. This categorization provided a comprehensive view of the factors contributing to the emergence of toxic assets.
<strong>Conclusion:</strong> The results highlight that toxic asset emergence is influenced by a wide range of factors, from managerial functions to broader economic and structural aspects. While strategies such as information concealment or the positive persuasion of stakeholders’ informational needs may temporarily mitigate the negative effects of toxic assets, their prolonged accumulation often results in a cascade of negative consequences. Companies face increased risks of stock price crashes due to delayed information dissemination and the subsequent surge of negative news. Additionally, delays in selling these assets, even at prices below their nominal value, exacerbate financial losses due to the lack of a suitable trading market. These findings underscore the critical need for proactive strategies to address toxic asset accumulation, thereby minimizing risks and safeguarding financial stability.https://jfmp.sbu.ac.ir/article_105029_c453a19ce8015d7b77ea2e23e63b522c.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637144620240822Stock Price Manipulation in the Iran Stock Market Using VAE-LSTM Hybrid ModelStock Price Manipulation in the Iran Stock Market Using VAE-LSTM Hybrid Model13316110513110.48308/jfmp.2024.105131FASeyed MohammadrezaHabibzadehPh.D. Candidate in Financial Engineering , Qom branch, Islamic Azad University, Qom, Iran.0009-0004-0244-8439Mohammad AilRastegarAssistant Prof., Department of System and Productivity Management, Tarbiat Modares University, Tehran, Iran0000-0001-5094-602xRezaGolami JamkarniAssistant Prof., Department of Accounting, Qom branch, Islamic Azad University, Qom, Iran.Sayyed KazemChavoshiAssistant Prof., Department of Financial Management, University of Kharazmi, Tehran, Iran.0000-0002-7852-0132Journal Article20240901<strong>Purpose:</strong> The stock market, as one of the main economic sectors of countries, plays an important role in the development and expansion of economic activity. With the development of technology and complex trading algorithms, stock manipulation has become more easily, which makes the use of tools such as artificial intelligence and deep learning to identify manipulation by supervise institutions inevitable. The aim of this research is to identify stock manipulation in the Iran stock market. For this purpose, information on 73 stocks from 19 industries admitted to the stock exchange during 1398 to 1402, approximately 71,300 trading days, was used.<br /><strong>Method: </strong>Identifying manipulation in stock transactions poses a significant challenge due to the temporal correlation of stock price data and its dynamic. This challenge is also exacerbated by the unavailability of labeled data. Therefore, given the lack of announcement of manipulated stocks by the stock exchange supervise in the Iran stock market, data identification: 1) Statistical tests such as abnormal returns, manipulated stocks, and the exact date of manipulation have been determined. 2) Random data simulating the stock manipulation pattern has been injected into the time series of stocks that have not been manipulated with high confidence (expert questionnaire). In the next step, using a combination of variable autoencoding models and long short-term memory, the VAE-LSTM algorithm has been designed to compare with some machine learning models such as decision tree, random forest, logistic regression, etc., which calculates the probability of stock manipulation.<br /><strong>Findings:</strong> After running the models, the accuracy and recall indices and F<sub>1</sub> and F<sub>2</sub> were calculated. Because in the stock market, the classification of manipulated and unmanipulated stocks is not of equal importance, the performance evaluation index F<sub>2</sub> has been used to rank the models. In order, the VAE-LSTM, decision tree, random forest, multilayer neural network, support vector machine, and logistic regression models showed better performance. The approximate F<sub>2</sub> values of the mentioned models are: 72%, 69%, 50%, 41%, 4% and 26%, respectively. Findings: After implementing the deep learning models, the accuracy and recall indices and F<sub>1</sub> and F<sub>2</sub> were calculated. Because in the capital market, the classification of manipulated and unmanipulated stocks is not of equal importance, the performance evaluation index F<sub>2</sub> was used to rank the models. The VAE-LSTM, decision tree, random forest, multilayer neural network, support vector machine, and logistic regression models performed better, respectively. The approximate F<sub>2</sub> values of the aforementioned models were: 72%, 69%, 50%, 41%, 40%, and 26%. After the VAE-LSTM hybrid model, the decision tree model is ranked next, which also has a good balance between the accuracy and recall indices. This indicates that perhaps one of the most effective ways to identify manipulation is to use predetermined rules that are extracted by decision tree models and can be updated at different time intervals.<br /><strong>Conclusion:</strong> Finally, the proposed model based on the F<sub>2 </sub>performance evaluation index has shown a better ability to detect manipulation than other models. It is important to note that other machine learning models used in this study also performed well, especially in the accuracy evaluation index, but unfortunately, they performed poorly in terms of the more important recall performance index. After determining the proposed model as the selected model, based on the Tehran Stock Exchange's total index, we considered the capital market's bullish period in the period from 1398/12/01 to 1399/05/31, the capital market's bearish period in the period from 1399/05/21 to 1399/08/20, and the year 1400 as the capital market's equilibrium period. As expected, the probability of manipulation is higher in bullish, balanced, and bearish markets, respectively. These results are generally consistent with other previous studies. The results are conceptually consistent with reality. Since short selling is not possible in the Iranian capital market, manipulators can only make a profit by manipulating by “raising the price and emptying” and it is not possible to use the manipulation method of “lowering the price and buying back”. Therefore, in a bear market, creating a trend change in the capital market requires a lot of resources, which reduces the incentive to manipulate the share.<strong>Purpose:</strong> The stock market, as one of the main economic sectors of countries, plays an important role in the development and expansion of economic activity. With the development of technology and complex trading algorithms, stock manipulation has become more easily, which makes the use of tools such as artificial intelligence and deep learning to identify manipulation by supervise institutions inevitable. The aim of this research is to identify stock manipulation in the Iran stock market. For this purpose, information on 73 stocks from 19 industries admitted to the stock exchange during 1398 to 1402, approximately 71,300 trading days, was used.<br /><strong>Method: </strong>Identifying manipulation in stock transactions poses a significant challenge due to the temporal correlation of stock price data and its dynamic. This challenge is also exacerbated by the unavailability of labeled data. Therefore, given the lack of announcement of manipulated stocks by the stock exchange supervise in the Iran stock market, data identification: 1) Statistical tests such as abnormal returns, manipulated stocks, and the exact date of manipulation have been determined. 2) Random data simulating the stock manipulation pattern has been injected into the time series of stocks that have not been manipulated with high confidence (expert questionnaire). In the next step, using a combination of variable autoencoding models and long short-term memory, the VAE-LSTM algorithm has been designed to compare with some machine learning models such as decision tree, random forest, logistic regression, etc., which calculates the probability of stock manipulation.<br /><strong>Findings:</strong> After running the models, the accuracy and recall indices and F<sub>1</sub> and F<sub>2</sub> were calculated. Because in the stock market, the classification of manipulated and unmanipulated stocks is not of equal importance, the performance evaluation index F<sub>2</sub> has been used to rank the models. In order, the VAE-LSTM, decision tree, random forest, multilayer neural network, support vector machine, and logistic regression models showed better performance. The approximate F<sub>2</sub> values of the mentioned models are: 72%, 69%, 50%, 41%, 4% and 26%, respectively. Findings: After implementing the deep learning models, the accuracy and recall indices and F<sub>1</sub> and F<sub>2</sub> were calculated. Because in the capital market, the classification of manipulated and unmanipulated stocks is not of equal importance, the performance evaluation index F<sub>2</sub> was used to rank the models. The VAE-LSTM, decision tree, random forest, multilayer neural network, support vector machine, and logistic regression models performed better, respectively. The approximate F<sub>2</sub> values of the aforementioned models were: 72%, 69%, 50%, 41%, 40%, and 26%. After the VAE-LSTM hybrid model, the decision tree model is ranked next, which also has a good balance between the accuracy and recall indices. This indicates that perhaps one of the most effective ways to identify manipulation is to use predetermined rules that are extracted by decision tree models and can be updated at different time intervals.<br /><strong>Conclusion:</strong> Finally, the proposed model based on the F<sub>2 </sub>performance evaluation index has shown a better ability to detect manipulation than other models. It is important to note that other machine learning models used in this study also performed well, especially in the accuracy evaluation index, but unfortunately, they performed poorly in terms of the more important recall performance index. After determining the proposed model as the selected model, based on the Tehran Stock Exchange's total index, we considered the capital market's bullish period in the period from 1398/12/01 to 1399/05/31, the capital market's bearish period in the period from 1399/05/21 to 1399/08/20, and the year 1400 as the capital market's equilibrium period. As expected, the probability of manipulation is higher in bullish, balanced, and bearish markets, respectively. These results are generally consistent with other previous studies. The results are conceptually consistent with reality. Since short selling is not possible in the Iranian capital market, manipulators can only make a profit by manipulating by “raising the price and emptying” and it is not possible to use the manipulation method of “lowering the price and buying back”. Therefore, in a bear market, creating a trend change in the capital market requires a lot of resources, which reduces the incentive to manipulate the share.https://jfmp.sbu.ac.ir/article_105131_99d4a5d47755bb360a44e7e7d7be7136.pdfShahid Beheshti UniversityFinancial Management Perspective2645-4637144620240822The Effect of Central Bank Policies (Interbank Interest Rate) on Capital Market Performance with the Dynamic Stochastic General Equilibrium ModelThe Effect of Central Bank Policies (Interbank Interest Rate) on Capital Market Performance with the Dynamic Stochastic General Equilibrium Model16319010545910.48308/jfmp.2024.105459FAZoleikhaMorsali ArzanaghPh.D. Candidate, Department of Economics, Aras International Campos of University of Tehran, Iran, Iran0000-0002-0611-029xMohsenMehraraProfessor, Department of Economics, University of Tehran, Tehran, Iran.YazdanGudarzi FarahaniAssistant Prof., Department of Islamic Economics, University of Qom, Qom.0000-0002-6551-776XJournal Article20240730<strong>Purpose:</strong> The stock market plays a multidimensional role in monetary policy decisions. On the other hand, the performance of the capital market is influenced by monetary policy innovations through various channels, as well as the value of corporate shares, which reflects the extent of economic developments and, in turn, indicates the power of monetary policies in guiding political decisions. In view of this, capital market efficiency not only reflects monetary policy decisions and the effects of the economy, but also provides a reflection for the Central Bank on the expectations of the private sector about the future and, of course, other key macroeconomic variables. The aim of this article is to examine the impact of central bank policies on capital market performance using the dynamic stochastic general equilibrium model approach..
<strong>Method: </strong>In this study, statistical data from the period 1368-1402 based on the frequency of seasonal data has been used. The method used in this study is to solve the Dynamic Stochastic General Equilibrium (DSGE) model. This dynamic stochastic general equilibrium model presents a new Keynesian approach under the hypothesis of sticky prices and monopolistic competition conditions for the Iranian economy. In general equilibrium models, the economic structure is extracted based on the optimization of economic units. Based on this type of modeling, it is assumed that there are three different economic units in the economy, each of which seeks to optimize its own goal; these three units are the consumer (household), the producer (firm), and the economic policymaker (which can be the government or the central bank). From the optimization of the behavior of these three economic units, macroeconomic structural equations are obtained. In general, from the optimization of the behavior of the household, the aggregate demand function is obtained, from the optimization of the behavior of the firm, the aggregate supply function is obtained, and from the optimization of the behavior of the policymaker, the policy function is obtained. The use of the term general equilibrium is because the consumption and production sectors are examined simultaneously and their decisions are specified and determined in a single structure. This modeling method has the advantage that the effects of variables on each other can be observed simultaneously and the model shocks can be well modeled and their effects can be seen.
<strong>Findings:</strong> The results of the monetary policy shock in this study showed that due to the existence of imperfections in financial markets, it leads to volatility and instability in the capital market. One of the shocks designed in this study is a change in the interest rate related to the central bank's overdraft, which accordingly, with an increase in the interest rate and the adjustment cost related to the failure to comply with capital adequacy and the bank's overdraft from the central bank, has led to a decrease in performance and efficiency in the capital market.
<strong>Conclusion:</strong> According to the results obtained, it is suggested that capital market activists pay special attention to the monetary policies of the central bank. Also, considering that changes in asset prices are considered as a reaction to changes in monetary policy and it is attributed to the immediate reaction of the stock market to changes in interest rates, this variable should be considered for decision-making. In addition, the use of the capital market and its mechanism of influence on the economy can lead to a decrease in the inflation rate and an increase in government income through adjustments in inflation expectations and individual money demand, and move the economy towards a systemic economy.<strong>Purpose:</strong> The stock market plays a multidimensional role in monetary policy decisions. On the other hand, the performance of the capital market is influenced by monetary policy innovations through various channels, as well as the value of corporate shares, which reflects the extent of economic developments and, in turn, indicates the power of monetary policies in guiding political decisions. In view of this, capital market efficiency not only reflects monetary policy decisions and the effects of the economy, but also provides a reflection for the Central Bank on the expectations of the private sector about the future and, of course, other key macroeconomic variables. The aim of this article is to examine the impact of central bank policies on capital market performance using the dynamic stochastic general equilibrium model approach..
<strong>Method: </strong>In this study, statistical data from the period 1368-1402 based on the frequency of seasonal data has been used. The method used in this study is to solve the Dynamic Stochastic General Equilibrium (DSGE) model. This dynamic stochastic general equilibrium model presents a new Keynesian approach under the hypothesis of sticky prices and monopolistic competition conditions for the Iranian economy. In general equilibrium models, the economic structure is extracted based on the optimization of economic units. Based on this type of modeling, it is assumed that there are three different economic units in the economy, each of which seeks to optimize its own goal; these three units are the consumer (household), the producer (firm), and the economic policymaker (which can be the government or the central bank). From the optimization of the behavior of these three economic units, macroeconomic structural equations are obtained. In general, from the optimization of the behavior of the household, the aggregate demand function is obtained, from the optimization of the behavior of the firm, the aggregate supply function is obtained, and from the optimization of the behavior of the policymaker, the policy function is obtained. The use of the term general equilibrium is because the consumption and production sectors are examined simultaneously and their decisions are specified and determined in a single structure. This modeling method has the advantage that the effects of variables on each other can be observed simultaneously and the model shocks can be well modeled and their effects can be seen.
<strong>Findings:</strong> The results of the monetary policy shock in this study showed that due to the existence of imperfections in financial markets, it leads to volatility and instability in the capital market. One of the shocks designed in this study is a change in the interest rate related to the central bank's overdraft, which accordingly, with an increase in the interest rate and the adjustment cost related to the failure to comply with capital adequacy and the bank's overdraft from the central bank, has led to a decrease in performance and efficiency in the capital market.
<strong>Conclusion:</strong> According to the results obtained, it is suggested that capital market activists pay special attention to the monetary policies of the central bank. Also, considering that changes in asset prices are considered as a reaction to changes in monetary policy and it is attributed to the immediate reaction of the stock market to changes in interest rates, this variable should be considered for decision-making. In addition, the use of the capital market and its mechanism of influence on the economy can lead to a decrease in the inflation rate and an increase in government income through adjustments in inflation expectations and individual money demand, and move the economy towards a systemic economy.https://jfmp.sbu.ac.ir/article_105459_45d90e7eb4fb87f9593f02e7518e41d4.pdf