University of Isfahan Journal of Asset Management and Financing 2383-1189 14 2 2026 06 22 Designing an Optimal Decision-Making Model for Investors: Integrating Artificial Intelligence and Financial Reporting Transparency Designing an Optimal Decision-Making Model for Investors: Integrating Artificial Intelligence and Financial Reporting Transparency 1 24 29331 10.22108/amf.2025.143308.1934 FA Sahar Abbas Hasan Ph.D. Candidate, Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran. Parviz Piri Associate Professor, Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran Pari Chalaki Assistant Professor, Department of Accounting, Faculty of Economics and Management, Urmia University, Urmia, Iran Journal Article 2024 11 11 Transparency and comparability of financial information constitute fundamental pillars of accountability and informed economic decision-making. With the rapid evolution of artificial intelligence (AI), this technology has significantly influenced financial and investment decision-making processes. This study seeks to design an optimal model for investor decision-making by exploring the integration of AI, with a particular focus on enhancing financial reporting transparency. A mixed-methods approach was adopted to achieve this objective. In the initial phase, qualitative data were gathered through in-depth interviews with 12 experts specializing in financial management and capital markets. Content analysis was employed to identify key themes and construct a preliminary model. To validate the model, a structured questionnaire was developed and distributed to 214 experts, yielding 200 valid responses. The questionnaire's reliability and validity were rigorously confirmed. Subsequently, structural equations modeling (SEM) was utilized to analyze the interrelationships among the variables. The findings revealed that all components of the proposed model, supported by both expert validation and statistical analysis, are robust and serve as critical factors in investor decision-making. Six key factors—financial, economic, political, psychological, individual, and artificial intelligence—were identified and validated as primary determinants influencing investment decisions. This study contributes to the literature by innovatively integrating AI with financial reporting transparency to enhance investment decision-making processes. The proposed model, which synthesizes these influential factors, offers novel perspectives for analyzing financial data and assists investors in achieving superior returns while mitigating risks. This framework can serve as a strategic tool for financial managers and stakeholders, enabling more precise and data-driven decision-making in complex financial environments. Keywords: Investor Decision-Making, Artificial Intelligence, Financial Reporting Transparency, Content Analysis JEL Classification: G11, C56, M41 Introduction Artificial intelligence (AI), a prominent branch of computer science, provides a robust framework for emulating human cognitive functions, including learning, pattern recognition, and decision-making. By harnessing advanced machine learning algorithms, neural networks, and natural language processing (NLP), AI systems are capable of analyzing extensive volumes of financial data with remarkable accuracy and efficiency. In this context, AI outperforms conventional methodologies by enabling the simultaneous processing of diverse data sources. Through the identification of hidden patterns and the prediction of market fluctuations, AI significantly enhances the quality of investor decision-making, particularly in the financial markets of developing countries. Key applications of AI in finance encompass the development of risk assessment models, the detection of fraudulent transactions, and the optimization of investment portfolios. Furthermore, by analyzing sentiment data extracted from news outlets and social media platforms, AI empowers investors to account for shifts in market sentiment, as well as economic and social dynamics. The integration of information technology systems not only improves the transparency of financial reporting but also ensures access to precise and timely information, thereby bolstering investor confidence and refining the decision-making process. This study, through a comprehensive review of prior research and the identification of existing gaps, seeks to propose an integrated model grounded in AI and financial reporting transparency. The primary objective of this model is to enhance the accuracy of financial forecasts and elevate the quality of investor decision-making. In this endeavor, critical investment factors such as risk, return, and liquidity are examined in conjunction with big data analytics to deliver innovative solutions that substantially strengthen investor trust in financial information and market trends. Method This study adopts a mixed-methods research design, comprising an initial qualitative phase followed by a quantitative phase. The qualitative phase utilizes content analysis, while the quantitative phase employs a correlative-survey methodology. The target sample for the qualitative phase consisted of professors and experts in financial management, as well as capital market specialists, all of whom possessed a foundational understanding of artificial intelligence (AI). Participants were selected through purposive sampling, and in-depth, semi-structured interviews were conducted. This approach, owing to its inherent flexibility, facilitated a thorough exploration of expert perspectives and the extraction of rich, nuanced data. The use of semi-structured interviews enabled the study not only to gather insights into the primary variables but also to uncover novel perspectives, previously unidentified factors, and latent relationships among variables. This contributed to the development of a robust theoretical framework and yielded findings with significant practical implications. The sampling process continued until theoretical saturation was achieved. Theoretical saturation refers to the point at which, following initial discoveries, the researcher continues data collection until the relationships between the main categories and subcategories become clear and meaningful. This process persisted until the researcher determined that additional interviews with experts (totaling 12 participants) no longer yielded new information. Table 1 provides a detailed overview of the demographic characteristics of the interviewees. Table (1) Demographic Characteristics of Interviewees No. Organization Position Education Gender 1 Capital Market Senior Manager Master of Accounting Male 2 University Professor Ph.D. in Accounting Male 3 University Professor Ph.D. in Financial Management Female 4 University Professor Ph.D. in Accounting Female 5 Capital Market Specialist Master of Finance Male 6 Capital Market Specialist Master of Accounting Male 7 Capital Market Middle Manager Ph.D. in Finance Male 8 University Professor Ph.D. in Accounting Male 9 Capital Market Accountant Master of Accounting Female 10 Capital Market Accountant Ph.D. in Finance Male 11 University Professor Ph.D. in Accounting Male 12 Capital Market Middle Manager Master of Financial Sciences Male The sample for the quantitative phase comprised all investors, buyers and sellers of stocks and other securities, and active participants in the stock market. A convenience sampling method was employed, guided by the structural equations modeling (SEM) formula, which recommends a minimum of 20 samples per latent variable and an overall minimum sample size of 200. Quantitative data were collected using a questionnaire consisting of 67 items measured on a 5-point Likert scale. Face validity was evaluated through consultations with 10 experts, and necessary revisions were implemented based on their feedback. In the qualitative phase, semi-structured interviews were conducted through in-person meetings, phone calls, and email correspondence. All interviews were recorded (with the explicit consent of participants) and subsequently transcribed and analyzed in detail. The credibility and validity of the findings were rigorously assessed using Patton’s evaluation criteria, which include credibility, transferability, and confirmability. Additional techniques such as triangulation through multiple sources, negative case analysis, and methodological flexibility were employed to enhance the robustness of the findings. The reliability of the model was evaluated using Kappa statistics, which involved comparing independent coding results to ensure consistency. For the quantitative data analysis, partial least squares (PLS) analysis was conducted using SmartPLS software. The analysis was divided into two components: the measurement model, which assessed the relationships between individual items and their corresponding dimensions, and the structural model, which examined the relationships between latent variables. Convergent validity was evaluated using the Average Variance Extracted (AVE), while discriminant validity was assessed through the Fornell-Larcker criterion and cross-loading tests. The reliability of the questionnaire was measured using Cronbach’s alpha, which yielded a value exceeding 0.7, indicating strong internal consistency. Findings In the initial phase of the study, data were gathered through semi-structured interviews with 12 experts to identify the key factors influencing investor decision-making in the context of artificial intelligence (AI) utilization. Content analysis was employed to analyze the interview data. Following an in-depth familiarization with the data, initial coding was conducted, yielding 67 preliminary codes. These codes were either explicitly or implicitly reflected in existing conceptual models. Subsequently, axial coding was performed to group related concepts and elucidate the relationships between categories. The outcomes of the initial and axial coding processes were systematically organized into a table of concepts pertinent to investor decision-making. The Kappa coefficient of 0.81 demonstrated excellent reliability of the model. Additionally, a two-round fuzzy Delphi approach was utilized to screen the identified indicators, with all indicators achieving scores above 7, confirming their relevance and validity. In the quantitative phase, the results of Cronbach’s alpha, composite reliability, and Average Variance Extracted (AVE) confirmed the model’s acceptable convergent validity and reliability. Discriminant validity was assessed using the Fornell-Larcker criterion, which revealed that the square root of the AVE for each construct exceeded its inter-construct correlations, further validating the model. Within the structural model, standardized path coefficients, t-statistics, and effect sizes (f²) were calculated for various paths, all of which indicated statistically significant relationships at the 99% confidence level. The predictive power of the structural model for investor decision-making was established using the Q² and R² indices, as well as the Goodness of Fit (GOF = 0.60 > 0.35). The analysis of the relationships between variables demonstrated that, in addition to the direct effects of economic, market psychology, political, individual, financial, and AI factors on decision-making, financial reporting transparency—as a moderating variable—also exerted a significant influence. Consequently, the evaluated model exhibited robust explanatory and predictive capabilities for investor behavior regarding AI adoption, supported by both theoretical and empirical evidence. Discussion and Conclusion Corporate investment, as a primary source of cash flow and a catalyst for economic development, plays a pivotal role in fostering long-term value creation. Access to accurate, transparent, and timely financial information has become increasingly critical in the investment decision-making process. Emerging technologies such as artificial intelligence (AI), with their exceptional processing capabilities and advanced analytical tools, offer more precise insights and mitigate uncertainties. This study seeks to propose an optimal model for investor decision-making by leveraging AI, with a particular emphasis on financial reporting transparency, as there is currently a lack of comprehensive models in the country that analyze the interplay of these factors. During the research process, 67 initial codes were extracted from in-depth interviews with experts and subsequently categorized into six primary themes: financial, economic, political, market psychology, individual, and AI factors. The findings, corroborated by prior studies, demonstrate that the use of AI significantly enhances the transparency and accuracy of financial reporting, streamlines investment decisions, and improves risk management. Numerous studies have confirmed the advantages of AI in increasing operational efficiency, predicting market trends, and optimizing portfolio selection. While AI holds transformative potential for financial decision-making, realizing this potential requires robust technical infrastructure, standardized data frameworks, and access to up-to-date information. Establishing regulatory frameworks, fostering international collaborations, and investing in workforce training are also essential for the successful implementation of this technology. In addition to proposing a comprehensive model, this study highlights existing challenges and limitations, suggesting that a gradual, context-sensitive implementation of AI can enhance investor confidence and contribute to the development of more transparent financial markets. Transparency and comparability of financial information constitute fundamental pillars of accountability and informed economic decision-making. With the rapid evolution of artificial intelligence (AI), this technology has significantly influenced financial and investment decision-making processes. This study seeks to design an optimal model for investor decision-making by exploring the integration of AI, with a particular focus on enhancing financial reporting transparency. A mixed-methods approach was adopted to achieve this objective. In the initial phase, qualitative data were gathered through in-depth interviews with 12 experts specializing in financial management and capital markets. Content analysis was employed to identify key themes and construct a preliminary model. To validate the model, a structured questionnaire was developed and distributed to 214 experts, yielding 200 valid responses. The questionnaire's reliability and validity were rigorously confirmed. Subsequently, structural equations modeling (SEM) was utilized to analyze the interrelationships among the variables. The findings revealed that all components of the proposed model, supported by both expert validation and statistical analysis, are robust and serve as critical factors in investor decision-making. Six key factors—financial, economic, political, psychological, individual, and artificial intelligence—were identified and validated as primary determinants influencing investment decisions. This study contributes to the literature by innovatively integrating AI with financial reporting transparency to enhance investment decision-making processes. The proposed model, which synthesizes these influential factors, offers novel perspectives for analyzing financial data and assists investors in achieving superior returns while mitigating risks. This framework can serve as a strategic tool for financial managers and stakeholders, enabling more precise and data-driven decision-making in complex financial environments. Keywords: Investor Decision-Making, Artificial Intelligence, Financial Reporting Transparency, Content Analysis JEL Classification: G11, C56, M41 Introduction Artificial intelligence (AI), a prominent branch of computer science, provides a robust framework for emulating human cognitive functions, including learning, pattern recognition, and decision-making. By harnessing advanced machine learning algorithms, neural networks, and natural language processing (NLP), AI systems are capable of analyzing extensive volumes of financial data with remarkable accuracy and efficiency. In this context, AI outperforms conventional methodologies by enabling the simultaneous processing of diverse data sources. Through the identification of hidden patterns and the prediction of market fluctuations, AI significantly enhances the quality of investor decision-making, particularly in the financial markets of developing countries. Key applications of AI in finance encompass the development of risk assessment models, the detection of fraudulent transactions, and the optimization of investment portfolios. Furthermore, by analyzing sentiment data extracted from news outlets and social media platforms, AI empowers investors to account for shifts in market sentiment, as well as economic and social dynamics. The integration of information technology systems not only improves the transparency of financial reporting but also ensures access to precise and timely information, thereby bolstering investor confidence and refining the decision-making process. This study, through a comprehensive review of prior research and the identification of existing gaps, seeks to propose an integrated model grounded in AI and financial reporting transparency. The primary objective of this model is to enhance the accuracy of financial forecasts and elevate the quality of investor decision-making. In this endeavor, critical investment factors such as risk, return, and liquidity are examined in conjunction with big data analytics to deliver innovative solutions that substantially strengthen investor trust in financial information and market trends. Method This study adopts a mixed-methods research design, comprising an initial qualitative phase followed by a quantitative phase. The qualitative phase utilizes content analysis, while the quantitative phase employs a correlative-survey methodology. The target sample for the qualitative phase consisted of professors and experts in financial management, as well as capital market specialists, all of whom possessed a foundational understanding of artificial intelligence (AI). Participants were selected through purposive sampling, and in-depth, semi-structured interviews were conducted. This approach, owing to its inherent flexibility, facilitated a thorough exploration of expert perspectives and the extraction of rich, nuanced data. The use of semi-structured interviews enabled the study not only to gather insights into the primary variables but also to uncover novel perspectives, previously unidentified factors, and latent relationships among variables. This contributed to the development of a robust theoretical framework and yielded findings with significant practical implications. The sampling process continued until theoretical saturation was achieved. Theoretical saturation refers to the point at which, following initial discoveries, the researcher continues data collection until the relationships between the main categories and subcategories become clear and meaningful. This process persisted until the researcher determined that additional interviews with experts (totaling 12 participants) no longer yielded new information. Table 1 provides a detailed overview of the demographic characteristics of the interviewees. Table (1) Demographic Characteristics of Interviewees No. Organization Position Education Gender 1 Capital Market Senior Manager Master of Accounting Male 2 University Professor Ph.D. in Accounting Male 3 University Professor Ph.D. in Financial Management Female 4 University Professor Ph.D. in Accounting Female 5 Capital Market Specialist Master of Finance Male 6 Capital Market Specialist Master of Accounting Male 7 Capital Market Middle Manager Ph.D. in Finance Male 8 University Professor Ph.D. in Accounting Male 9 Capital Market Accountant Master of Accounting Female 10 Capital Market Accountant Ph.D. in Finance Male 11 University Professor Ph.D. in Accounting Male 12 Capital Market Middle Manager Master of Financial Sciences Male The sample for the quantitative phase comprised all investors, buyers and sellers of stocks and other securities, and active participants in the stock market. A convenience sampling method was employed, guided by the structural equations modeling (SEM) formula, which recommends a minimum of 20 samples per latent variable and an overall minimum sample size of 200. Quantitative data were collected using a questionnaire consisting of 67 items measured on a 5-point Likert scale. Face validity was evaluated through consultations with 10 experts, and necessary revisions were implemented based on their feedback. In the qualitative phase, semi-structured interviews were conducted through in-person meetings, phone calls, and email correspondence. All interviews were recorded (with the explicit consent of participants) and subsequently transcribed and analyzed in detail. The credibility and validity of the findings were rigorously assessed using Patton’s evaluation criteria, which include credibility, transferability, and confirmability. Additional techniques such as triangulation through multiple sources, negative case analysis, and methodological flexibility were employed to enhance the robustness of the findings. The reliability of the model was evaluated using Kappa statistics, which involved comparing independent coding results to ensure consistency. For the quantitative data analysis, partial least squares (PLS) analysis was conducted using SmartPLS software. The analysis was divided into two components: the measurement model, which assessed the relationships between individual items and their corresponding dimensions, and the structural model, which examined the relationships between latent variables. Convergent validity was evaluated using the Average Variance Extracted (AVE), while discriminant validity was assessed through the Fornell-Larcker criterion and cross-loading tests. The reliability of the questionnaire was measured using Cronbach’s alpha, which yielded a value exceeding 0.7, indicating strong internal consistency. Findings In the initial phase of the study, data were gathered through semi-structured interviews with 12 experts to identify the key factors influencing investor decision-making in the context of artificial intelligence (AI) utilization. Content analysis was employed to analyze the interview data. Following an in-depth familiarization with the data, initial coding was conducted, yielding 67 preliminary codes. These codes were either explicitly or implicitly reflected in existing conceptual models. Subsequently, axial coding was performed to group related concepts and elucidate the relationships between categories. The outcomes of the initial and axial coding processes were systematically organized into a table of concepts pertinent to investor decision-making. The Kappa coefficient of 0.81 demonstrated excellent reliability of the model. Additionally, a two-round fuzzy Delphi approach was utilized to screen the identified indicators, with all indicators achieving scores above 7, confirming their relevance and validity. In the quantitative phase, the results of Cronbach’s alpha, composite reliability, and Average Variance Extracted (AVE) confirmed the model’s acceptable convergent validity and reliability. Discriminant validity was assessed using the Fornell-Larcker criterion, which revealed that the square root of the AVE for each construct exceeded its inter-construct correlations, further validating the model. Within the structural model, standardized path coefficients, t-statistics, and effect sizes (f²) were calculated for various paths, all of which indicated statistically significant relationships at the 99% confidence level. The predictive power of the structural model for investor decision-making was established using the Q² and R² indices, as well as the Goodness of Fit (GOF = 0.60 > 0.35). The analysis of the relationships between variables demonstrated that, in addition to the direct effects of economic, market psychology, political, individual, financial, and AI factors on decision-making, financial reporting transparency—as a moderating variable—also exerted a significant influence. Consequently, the evaluated model exhibited robust explanatory and predictive capabilities for investor behavior regarding AI adoption, supported by both theoretical and empirical evidence. Discussion and Conclusion Corporate investment, as a primary source of cash flow and a catalyst for economic development, plays a pivotal role in fostering long-term value creation. Access to accurate, transparent, and timely financial information has become increasingly critical in the investment decision-making process. Emerging technologies such as artificial intelligence (AI), with their exceptional processing capabilities and advanced analytical tools, offer more precise insights and mitigate uncertainties. This study seeks to propose an optimal model for investor decision-making by leveraging AI, with a particular emphasis on financial reporting transparency, as there is currently a lack of comprehensive models in the country that analyze the interplay of these factors. During the research process, 67 initial codes were extracted from in-depth interviews with experts and subsequently categorized into six primary themes: financial, economic, political, market psychology, individual, and AI factors. The findings, corroborated by prior studies, demonstrate that the use of AI significantly enhances the transparency and accuracy of financial reporting, streamlines investment decisions, and improves risk management. Numerous studies have confirmed the advantages of AI in increasing operational efficiency, predicting market trends, and optimizing portfolio selection. While AI holds transformative potential for financial decision-making, realizing this potential requires robust technical infrastructure, standardized data frameworks, and access to up-to-date information. Establishing regulatory frameworks, fostering international collaborations, and investing in workforce training are also essential for the successful implementation of this technology. In addition to proposing a comprehensive model, this study highlights existing challenges and limitations, suggesting that a gradual, context-sensitive implementation of AI can enhance investor confidence and contribute to the development of more transparent financial markets.

https://amf.ui.ac.ir/article_29331_7d3e9c102dbcef8c583c412005713819.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 14 2 2026 06 22 The Effect of Internal Control on the Relationship Between Financialization and the Probability of Corporate Financial Fraud (Tehran Stock Exchange Companies) The Effect of Internal Control on the Relationship Between Financialization and the Probability of Corporate Financial Fraud (Tehran Stock Exchange Companies) 25 40 29475 10.22108/amf.2025.142433.1949 FA Sirous Keshavarz Researcher, Management Research Center, Tarbiat Modares University, Tehran, Iran Zeinab Rezaee M. A degree, Department of Accounting, Faculty of Management, Feiz-e-Islam Institute of Higher Education, Khomeini shahr, Iran Parisa Alizadeh Assistant Professor, Science, Technology and Innovation Financing and Economics Department, National Research Institute for Science Policy (NRISP), Tehran, Iran Shahla Talari Assistant Professor, Department of Accounting, Faculty of Management, Aghigh Institute of Higher Education, Shahinshahr, Iran Journal Article 2025 01 13 The aim of this study is to examine the impact of internal control on the relationship between financialization and the likelihood of financial fraud in companies listed on the Tehran Stock Exchange. For this purpose, using a systematic sampling method, 190 listed companies were selected over the period from 2016 to 2022. Data were collected from Rahavard Novin software and the official website of the Tehran Stock Exchange. Logistic regression models were used to analyze the data, utilizing Eviews software. The results showed that corporate financialization has a positive and significant effect on the likelihood of financial fraud. However, based on the coefficients of the financialization variable, the probability of fraud is lower in companies with a low level of financialization compared to those with a high level. Moreover, internal control causes financialization to have a nonlinear negative impact on the likelihood of fraud, ultimately reducing it. The findings indicate that while financialization increases the likelihood of fraud—especially in highly financialized companies—the presence of internal control alongside financialization helps to mitigate the risk of fraud. The aim of this study is to examine the impact of internal control on the relationship between financialization and the likelihood of financial fraud in companies listed on the Tehran Stock Exchange. For this purpose, using a systematic sampling method, 190 listed companies were selected over the period from 2016 to 2022. Data were collected from Rahavard Novin software and the official website of the Tehran Stock Exchange. Logistic regression models were used to analyze the data, utilizing Eviews software. The results showed that corporate financialization has a positive and significant effect on the likelihood of financial fraud. However, based on the coefficients of the financialization variable, the probability of fraud is lower in companies with a low level of financialization compared to those with a high level. Moreover, internal control causes financialization to have a nonlinear negative impact on the likelihood of fraud, ultimately reducing it. The findings indicate that while financialization increases the likelihood of fraud—especially in highly financialized companies—the presence of internal control alongside financialization helps to mitigate the risk of fraud.

https://amf.ui.ac.ir/article_29475_990869ff3b105fae8a006924ed186ed6.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 14 2 2026 06 22 The Effect of Integrated Reporting on Audit Quality The Effect of Integrated Reporting on Audit Quality 41 58 29701 10.22108/amf.2025.144468.1962 FA Majid Hashemi Dehchi Assistant Professor, Department of Accounting, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran Naser Izadinia Associate Professor. Department of Accounting, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran Mansour Sadeghi Hosnijeh Ph.D. Student. Department of Accounting, Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran Journal Article 2025 02 22 This study examines the implications of the corporate transition from standalone financial reporting to integrated reporting (IR), which combines material financial and non-financial information, such as environmental risk disclosures. In light of recent revisions to international and national auditing standards that underscore the significance of non-financial information, this research specifically addresses the ambiguous relationship between IR adoption and audit quality within the Iranian context. Analyzing 858 firm-year observations from companies listed on the Tehran Stock Exchange (TSE) from 2015 to 2023 using the Generalized Least Squares (GLS) regression method, our findings demonstrate that integrated reporting exerts a positive and significant effect on audit quality. Furthermore, the adoption of IR is associated with a reduction in audit fees. This study consequently offers valuable insights for auditors and regulators by elucidating the cost-benefit dynamics of integrating non-financial information into corporate reporting. This study examines the implications of the corporate transition from standalone financial reporting to integrated reporting (IR), which combines material financial and non-financial information, such as environmental risk disclosures. In light of recent revisions to international and national auditing standards that underscore the significance of non-financial information, this research specifically addresses the ambiguous relationship between IR adoption and audit quality within the Iranian context. Analyzing 858 firm-year observations from companies listed on the Tehran Stock Exchange (TSE) from 2015 to 2023 using the Generalized Least Squares (GLS) regression method, our findings demonstrate that integrated reporting exerts a positive and significant effect on audit quality. Furthermore, the adoption of IR is associated with a reduction in audit fees. This study consequently offers valuable insights for auditors and regulators by elucidating the cost-benefit dynamics of integrating non-financial information into corporate reporting.

https://amf.ui.ac.ir/article_29701_c3fc4e842392e704f53aed9c457fabbd.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 14 2 2026 06 22 Providing An Appropriate Model for Islamic Project Financing Aligned with the Project Life Cycle Providing An Appropriate Model for Islamic Project Financing Aligned with the Project Life Cycle 59 84 29751 10.22108/amf.2025.144948.1975 FA Mohammad Ahmadi Beni Ph.D. Student, Department of Financial Management, Faculty of Islamic Studies and Management, Imam Sadiq University, Tehran, Iran Mohammad Noruzi Assistant Professor, Department of Operations and Productivity Management, Faculty of Islamic Studies and Management, Imam Sadiq University, Tehran, Iran Mohammad Tohidi Associate Professor, Department of Financial Management, Faculty of Islamic Studies and Management, Imam Sadiq University, Tehran, Iran Journal Article 2025 04 16 Project finance is a specialized funding mechanism for large-scale infrastructure and industrial projects, wherein financing is secured against the project's future cash flows, thereby mitigating sponsors' financial risk. Despite its advantages, the selection of an appropriate financing method presents a significant challenge, particularly within the context of Islamic finance, where strict adherence to Shariah principles is paramount. An unsuitable selection can lead to escalated costs, delays, and potential project failure. This study proposes a structured decision-making model to identify the most suitable Islamic project finance method. Employing a descriptive-analytical methodology, the research first excludes non-Shariah-compliant instruments. Subsequently, through a literature review, thirty key criteria influencing financing decisions are identified and categorized. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is then applied to rank twenty-three permissible financing instruments according to their applicability across the four primary phases of the project life cycle: initiation, planning, execution, and closure. The results include a phased prioritization framework that assists project sponsors and financiers in selecting Islamic financing instruments that are both economically efficient and compliant with Shariah principles. Keywords: Finance, Project Finance, Islamic Finance, Project Phases, Project Life Cycle. JEL Classification: G20, G32, O16, O22. Introduction Access to adequate financial resources is a fundamental prerequisite for economic development, particularly for funding the large-scale infrastructure projects that underpin growth (Brealey et al., 2011). In this context, project finance has emerged as a critical alternative to conventional corporate financing. It is especially vital for capital-intensive, high-risk ventures where traditional methods are often unsuitable due to balance sheet constraints and the burden of increased corporate indebtedness (Esty, 2008; Finnerty, 2013). The distinctiveness of project finance lies in its structure: funding is allocated directly to a legally independent project entity, with debt repayment deriving exclusively from the project's future cash flows, thereby ring-fencing risk for sponsors (Yescombe, 2014). Despite its recognized advantages, the selection of an appropriate financing model remains a complex and critical managerial decision. This challenge is particularly acute within Islamic financial jurisdictions, where many conventional debt-based instruments are impermissible under Shariah law due to prohibitions against interest (riba), excessive uncertainty (gharar), and speculative gain (maysir) (Warde, 2000). Consequently, Islamic project finance necessitates solutions meticulously tailored to comply with Fiqh al-Mu'amalat (Islamic commercial jurisprudence), requiring instruments that align with both religious principles and pragmatic legal standards (Ayub, 2019; Abdi & Mobini Dehkordi, 2020). Although a substantial body of literature examines individual Islamic finance instruments—such as Ijara, Murabaha, Istisna', and other Sukuk instruments—a significant gap exists in providing a holistic, phase-sensitive framework for their selection. Most studies address these instruments in isolation, without a structured methodology for aligning them with the distinct financial and operational requirements of each stage in the project life cycle. This study seeks to address this gap by developing a structured decision-making model that systematically prioritizes Shariah-compliant financing instruments across the initiation, planning, execution, and closure phases. The proposed framework aims to provide project sponsors and financiers with a robust tool to enhance not only Shariah compliance but also the overall economic viability and implementation efficiency of major projects. Materials & Methods This study was conducted in three sequential stages. First, a descriptive-analytical method was employed to identify and exclude conventional project finance instruments that are non-compliant with Shariah principles. A comprehensive literature review, supplemented by expert consultation, led to the identification of 23 permissible instruments, which were categorized into five primary groups: equity-based, debt-based, mezzanine, multilateral development bank finance, and Sukuk. In the second stage, a systematic review of 16 scholarly publications was conducted to extract 30 key criteria influencing Islamic project finance decisions. These criteria were clustered into six dimensions: financier-related, finance-related, instrument-specific characteristics, project-specific factors, risk management, and Shariah compliance. The criteria underwent validation by a panel of 20 financial professionals and academics, each possessing over five years of relevant experience. Content Validity Ratio (CVR), Content Validity Index (CVI), and Cronbach's alpha were used to confirm the validity and reliability of the criteria set. The third stage entailed the application of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model to evaluate the relative suitability of each financial instrument across the four project phases: initiation, planning, execution, and closure. Expert assessments, recorded using Likert scales, along with weight normalization, facilitated the ranking of instruments based on their proximity to ideal and anti-ideal solutions. This structured methodology enables a phase-specific prioritization that aligns instrument characteristics with the distinct needs and risk profiles of each project life cycle stage, thereby providing a practical and adaptable decision-support tool for Shariah-compliant project financing. Findings The study identified a total of 23 Shariah-compliant financial instruments. These encompassed equity, preferred shares, Shariah-compliant bank loans, syndicated loans, project-specific Islamic financing, and various Sukuk structures, including those based on Ijara, Istisna’, Murabaha, and hybrid instruments. The application of the TOPSIS analysis yielded a ranking of these instruments based on their alignment with the 30 weighted criteria across the four distinct project phases. Analysis of the initiation phase indicated that bank loans, Salam contracts, and Istisna’ were ranked highest, attributable to their reliability, structural simplicity, and efficacy in securing initial capital. Equity-based instruments received a moderate ranking, reflecting the higher risk exposure for financiers at this early stage. During the planning phase, where cost efficiency and exchange rate risk mitigation were prioritized, Salam, bank loans, and multilateral development bank loans emerged as the most suitable. In the execution phase, the critical criteria shifted to liquidity, comprehensive project cost coverage, and operational risk management. Consequently, Salam and bank loans again led the rankings, followed closely by equity and Istisna’ contracts. For the closure phase, instruments offering strong liquidity and alignment with long-term project horizons were preferred, with multilateral development bank loans, bank loans, and Salam retaining the top positions. A consistent pattern across all phases was the lower ranking of more complex instruments, such as combined Sukuk structures (e.g., Musharakah–Ijara), warrants, and export credit facilities, due to their structural intricacy and associated compliance challenges. These findings provide a nuanced framework for matching Islamic financial tools to the evolving requirements of infrastructure projects, thereby offering a pathway to optimize both Shariah compliance and project performance. Discussion and Conclusion The findings of this study contribute a structured, phase-sensitive model for aligning Islamic financial instruments with the dynamic requirements of infrastructure projects across their life cycle. By systematically excluding non-Shariah-compliant options and identifying criteria critical to Islamic financing decisions, this research provides a tailored decision-making framework for stakeholders in Islamic economies. The results underscore that no single instrument is universally optimal across all project phases. Instead, effective project finance necessitates a dynamic portfolio of tools that adapts to the project's evolving financial, operational, and risk profile. The consistent high ranking of instruments such as bank loans, Salam, and Istisna‘ can be attributed to their operational simplicity, contractual flexibility, and robust conformity with established Islamic jurisprudence, making them particularly versatile. This framework carries significant practical implications. It offers governments, financial institutions, and project developers in Muslim-majority nations a systematic approach for structuring financing packages that are not only economically efficient but also rigorously aligned with ethical and religious principles. Several promising avenues for future research are recommended. These include applying the model to sector-specific case studies (e.g., renewable energy, transportation); investigating distinctions between public and private project finance; incorporating macroeconomic variables such as inflation and currency volatility; and expanding the model to encompass emerging Islamic finance instruments. Furthermore, the framework should be periodically updated to integrate innovative financial tools as they gain acceptance. By integrating the principles of Islamic finance with lifecycle-based project management theory, this study addresses a critical gap in the literature and lays the groundwork for more adaptive, compliant, and strategic infrastructure financing within the Islamic world. Project finance is a specialized funding mechanism for large-scale infrastructure and industrial projects, wherein financing is secured against the project's future cash flows, thereby mitigating sponsors' financial risk. Despite its advantages, the selection of an appropriate financing method presents a significant challenge, particularly within the context of Islamic finance, where strict adherence to Shariah principles is paramount. An unsuitable selection can lead to escalated costs, delays, and potential project failure. This study proposes a structured decision-making model to identify the most suitable Islamic project finance method. Employing a descriptive-analytical methodology, the research first excludes non-Shariah-compliant instruments. Subsequently, through a literature review, thirty key criteria influencing financing decisions are identified and categorized. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is then applied to rank twenty-three permissible financing instruments according to their applicability across the four primary phases of the project life cycle: initiation, planning, execution, and closure. The results include a phased prioritization framework that assists project sponsors and financiers in selecting Islamic financing instruments that are both economically efficient and compliant with Shariah principles. Keywords: Finance, Project Finance, Islamic Finance, Project Phases, Project Life Cycle. JEL Classification: G20, G32, O16, O22. Introduction Access to adequate financial resources is a fundamental prerequisite for economic development, particularly for funding the large-scale infrastructure projects that underpin growth (Brealey et al., 2011). In this context, project finance has emerged as a critical alternative to conventional corporate financing. It is especially vital for capital-intensive, high-risk ventures where traditional methods are often unsuitable due to balance sheet constraints and the burden of increased corporate indebtedness (Esty, 2008; Finnerty, 2013). The distinctiveness of project finance lies in its structure: funding is allocated directly to a legally independent project entity, with debt repayment deriving exclusively from the project's future cash flows, thereby ring-fencing risk for sponsors (Yescombe, 2014). Despite its recognized advantages, the selection of an appropriate financing model remains a complex and critical managerial decision. This challenge is particularly acute within Islamic financial jurisdictions, where many conventional debt-based instruments are impermissible under Shariah law due to prohibitions against interest (riba), excessive uncertainty (gharar), and speculative gain (maysir) (Warde, 2000). Consequently, Islamic project finance necessitates solutions meticulously tailored to comply with Fiqh al-Mu'amalat (Islamic commercial jurisprudence), requiring instruments that align with both religious principles and pragmatic legal standards (Ayub, 2019; Abdi & Mobini Dehkordi, 2020). Although a substantial body of literature examines individual Islamic finance instruments—such as Ijara, Murabaha, Istisna', and other Sukuk instruments—a significant gap exists in providing a holistic, phase-sensitive framework for their selection. Most studies address these instruments in isolation, without a structured methodology for aligning them with the distinct financial and operational requirements of each stage in the project life cycle. This study seeks to address this gap by developing a structured decision-making model that systematically prioritizes Shariah-compliant financing instruments across the initiation, planning, execution, and closure phases. The proposed framework aims to provide project sponsors and financiers with a robust tool to enhance not only Shariah compliance but also the overall economic viability and implementation efficiency of major projects. Materials & Methods This study was conducted in three sequential stages. First, a descriptive-analytical method was employed to identify and exclude conventional project finance instruments that are non-compliant with Shariah principles. A comprehensive literature review, supplemented by expert consultation, led to the identification of 23 permissible instruments, which were categorized into five primary groups: equity-based, debt-based, mezzanine, multilateral development bank finance, and Sukuk. In the second stage, a systematic review of 16 scholarly publications was conducted to extract 30 key criteria influencing Islamic project finance decisions. These criteria were clustered into six dimensions: financier-related, finance-related, instrument-specific characteristics, project-specific factors, risk management, and Shariah compliance. The criteria underwent validation by a panel of 20 financial professionals and academics, each possessing over five years of relevant experience. Content Validity Ratio (CVR), Content Validity Index (CVI), and Cronbach's alpha were used to confirm the validity and reliability of the criteria set. The third stage entailed the application of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model to evaluate the relative suitability of each financial instrument across the four project phases: initiation, planning, execution, and closure. Expert assessments, recorded using Likert scales, along with weight normalization, facilitated the ranking of instruments based on their proximity to ideal and anti-ideal solutions. This structured methodology enables a phase-specific prioritization that aligns instrument characteristics with the distinct needs and risk profiles of each project life cycle stage, thereby providing a practical and adaptable decision-support tool for Shariah-compliant project financing. Findings The study identified a total of 23 Shariah-compliant financial instruments. These encompassed equity, preferred shares, Shariah-compliant bank loans, syndicated loans, project-specific Islamic financing, and various Sukuk structures, including those based on Ijara, Istisna’, Murabaha, and hybrid instruments. The application of the TOPSIS analysis yielded a ranking of these instruments based on their alignment with the 30 weighted criteria across the four distinct project phases. Analysis of the initiation phase indicated that bank loans, Salam contracts, and Istisna’ were ranked highest, attributable to their reliability, structural simplicity, and efficacy in securing initial capital. Equity-based instruments received a moderate ranking, reflecting the higher risk exposure for financiers at this early stage. During the planning phase, where cost efficiency and exchange rate risk mitigation were prioritized, Salam, bank loans, and multilateral development bank loans emerged as the most suitable. In the execution phase, the critical criteria shifted to liquidity, comprehensive project cost coverage, and operational risk management. Consequently, Salam and bank loans again led the rankings, followed closely by equity and Istisna’ contracts. For the closure phase, instruments offering strong liquidity and alignment with long-term project horizons were preferred, with multilateral development bank loans, bank loans, and Salam retaining the top positions. A consistent pattern across all phases was the lower ranking of more complex instruments, such as combined Sukuk structures (e.g., Musharakah–Ijara), warrants, and export credit facilities, due to their structural intricacy and associated compliance challenges. These findings provide a nuanced framework for matching Islamic financial tools to the evolving requirements of infrastructure projects, thereby offering a pathway to optimize both Shariah compliance and project performance. Discussion and Conclusion The findings of this study contribute a structured, phase-sensitive model for aligning Islamic financial instruments with the dynamic requirements of infrastructure projects across their life cycle. By systematically excluding non-Shariah-compliant options and identifying criteria critical to Islamic financing decisions, this research provides a tailored decision-making framework for stakeholders in Islamic economies. The results underscore that no single instrument is universally optimal across all project phases. Instead, effective project finance necessitates a dynamic portfolio of tools that adapts to the project's evolving financial, operational, and risk profile. The consistent high ranking of instruments such as bank loans, Salam, and Istisna‘ can be attributed to their operational simplicity, contractual flexibility, and robust conformity with established Islamic jurisprudence, making them particularly versatile. This framework carries significant practical implications. It offers governments, financial institutions, and project developers in Muslim-majority nations a systematic approach for structuring financing packages that are not only economically efficient but also rigorously aligned with ethical and religious principles. Several promising avenues for future research are recommended. These include applying the model to sector-specific case studies (e.g., renewable energy, transportation); investigating distinctions between public and private project finance; incorporating macroeconomic variables such as inflation and currency volatility; and expanding the model to encompass emerging Islamic finance instruments. Furthermore, the framework should be periodically updated to integrate innovative financial tools as they gain acceptance. By integrating the principles of Islamic finance with lifecycle-based project management theory, this study addresses a critical gap in the literature and lays the groundwork for more adaptive, compliant, and strategic infrastructure financing within the Islamic world.

https://amf.ui.ac.ir/article_29751_7f93042c38351bbcc0904a818cb06eae.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 14 2 2026 06 22 The Impact of Managerial Narcissism and Ability on the Cost of Debt The Impact of Managerial Narcissism and Ability on the Cost of Debt 85 108 29737 10.22108/amf.2025.144963.1976 FA Hasan Fattahi Nafchi Assistant Professor, Department of Accounting, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran Puriya Zivari Kamran Ph.D. Student, Science and Research Branch, Islamic Azad University, Tehran, Iran Farshid Riahi Dorcheh M.A., Faculty of Agricultural Economics, University of Tehran, Tehran, Iran Journal Article 2025 04 18 This study examines the influence of managerial personality traits—specifically narcissism and managerial ability—on corporate debt costs. It is posited that financial institutions perceive narcissistic managers as riskier, resulting in higher borrowing costs. Conversely, managerial ability enhances decision-making quality and mitigates agency conflicts, thereby reducing the cost of debt. The analysis draws on data from 129 firms listed on the Tehran Stock Exchange between 2015 and 2022. Narcissism is measured using both signature size and a psychological signature index. The findings indicate a positive association between managerial narcissism and debt costs, while managerial ability exhibits a negative relationship. Notably, the interaction between narcissism and ability is statistically insignificant when narcissism is measured by signature size but becomes significant when using the psychological index. This suggests that managerial ability can attenuate the adverse effects of narcissism. The results further identify highly capable, non-narcissistic managers as the most effective profile for minimizing debt costs. Additional analyses reveal a U-shaped nonlinear relationship between narcissism and the cost of debt, as well as a linear inverse relationship between ability and debt cost. This study contributes to the existing literature by incorporating a psychology-based measure of narcissism and by exploring the joint impact of managerial traits. The findings offer meaningful implications for financial decision-making and credit risk assessment. Keywords: Managerial Narcissism, Managerial Ability, Cost of Debt. JEL Classification: G32, D22, M12, D91. Introduction Recent literature highlights growing concerns regarding executive narcissism and its impact on firm outcomes, particularly the cost of debt. While narcissistic traits may promote innovation and attract market attention, they can also exacerbate agency conflicts and increase risk-taking behavior, thereby elevating external financing costs. In contrast, managerial ability is widely regarded as a mitigating factor, given its role in enhancing strategic decision-making and promoting transparent financial reporting. However, prior research has produced mixed results, particularly in emerging markets such as Iran. This study aims to reconcile these divergent findings by examining whether managerial ability can offset the detrimental effects of narcissism on the cost of debt. It employs a psychological measure of narcissism alongside the conventional signature size metric and incorporates a scenario-based framework to classify managers into distinct profiles—such as capable non-narcissists and incapable narcissists. Additionally, the study explores the potential for nonlinear dynamics in the relationship between narcissism and debt cost, thereby offering a more nuanced understanding beyond the traditionally assumed linear model. Materials & Methods This study is applied and correlational in nature, utilizing data from 129 companies listed on the Tehran Stock Exchange between 2015 and 2022. CEO narcissism is assessed using two methods: (1) the natural logarithm of the CEO’s signature area, measured via ImageJ software, and (2) a psychological index based on signature characteristics—such as complexity, presence of vertical lines, inclusion of the CEO’s name, and counterclockwise orientation—scored on a scale from 0 to 4. Managerial ability is evaluated using the Demerjian DEA-based model, where inputs include cost of goods sold (COGS), selling, general and administrative expenses (SG&A), fixed assets, and intangible assets, with sales serving as the output. The efficiency score derived from this model is then regressed on firm-specific variables, and the residuals are used as a proxy for managerial ability. The cost of debt (COD) is calculated by dividing interest expense by total liabilities. Panel data regressions with robust standard errors are employed, controlling for firm size, leverage, profitability, board independence, CEO tenure, CEO duality, and both industry and year fixed effects. Findings The results indicate that managerial narcissism is associated with an increase in the cost of debt, whereas managerial ability has a mitigating effect, leading to lower debt costs. However, the interaction between narcissism and managerial ability is not statistically significant when narcissism is measured by signature size. In contrast, when narcissism is assessed using a psychological signature index, the interaction becomes significant, suggesting that managerial ability can offset the adverse effects of narcissism on the cost of debt. Furthermore, when managers are categorized into distinct profiles—capable non-narcissists, incapable narcissists, and others—capable non-narcissistic managers emerge as the most effective in minimizing debt costs. Additional analyses reveal a U-shaped nonlinear relationship between managerial narcissism and the cost of debt, while managerial ability maintains a consistently negative linear association with debt costs. Discussion and Conclusion The findings highlight the complex and sometimes opposing roles of managerial traits in shaping corporate financing outcomes. While narcissism is often viewed as detrimental, it may offer certain advantages at moderate levels by fostering confidence and promoting innovation. However, excessive narcissism amplifies risk-taking and agency conflicts, ultimately leading to higher debt costs. In contrast, managerial ability consistently mitigates financial risk through enhanced decision-making and greater transparency. The significant interaction between the two traits suggests that managerial ability can buffer the negative effects of narcissism. The use of a psychological index to measure narcissism adds a nuanced perspective, uncovering associations that conventional metrics may overlook. These insights underscore the importance for policymakers, investors, and creditors to consider both psychological and competence-based evaluations of executives when assessing corporate risk and governance quality. Overall, the study contributes to the literature on behavioral finance and managerial decision-making in emerging markets, emphasizing the utility of multidimensional executive profiling in financial analysis. This study examines the influence of managerial personality traits—specifically narcissism and managerial ability—on corporate debt costs. It is posited that financial institutions perceive narcissistic managers as riskier, resulting in higher borrowing costs. Conversely, managerial ability enhances decision-making quality and mitigates agency conflicts, thereby reducing the cost of debt. The analysis draws on data from 129 firms listed on the Tehran Stock Exchange between 2015 and 2022. Narcissism is measured using both signature size and a psychological signature index. The findings indicate a positive association between managerial narcissism and debt costs, while managerial ability exhibits a negative relationship. Notably, the interaction between narcissism and ability is statistically insignificant when narcissism is measured by signature size but becomes significant when using the psychological index. This suggests that managerial ability can attenuate the adverse effects of narcissism. The results further identify highly capable, non-narcissistic managers as the most effective profile for minimizing debt costs. Additional analyses reveal a U-shaped nonlinear relationship between narcissism and the cost of debt, as well as a linear inverse relationship between ability and debt cost. This study contributes to the existing literature by incorporating a psychology-based measure of narcissism and by exploring the joint impact of managerial traits. The findings offer meaningful implications for financial decision-making and credit risk assessment. Keywords: Managerial Narcissism, Managerial Ability, Cost of Debt. JEL Classification: G32, D22, M12, D91. Introduction Recent literature highlights growing concerns regarding executive narcissism and its impact on firm outcomes, particularly the cost of debt. While narcissistic traits may promote innovation and attract market attention, they can also exacerbate agency conflicts and increase risk-taking behavior, thereby elevating external financing costs. In contrast, managerial ability is widely regarded as a mitigating factor, given its role in enhancing strategic decision-making and promoting transparent financial reporting. However, prior research has produced mixed results, particularly in emerging markets such as Iran. This study aims to reconcile these divergent findings by examining whether managerial ability can offset the detrimental effects of narcissism on the cost of debt. It employs a psychological measure of narcissism alongside the conventional signature size metric and incorporates a scenario-based framework to classify managers into distinct profiles—such as capable non-narcissists and incapable narcissists. Additionally, the study explores the potential for nonlinear dynamics in the relationship between narcissism and debt cost, thereby offering a more nuanced understanding beyond the traditionally assumed linear model. Materials & Methods This study is applied and correlational in nature, utilizing data from 129 companies listed on the Tehran Stock Exchange between 2015 and 2022. CEO narcissism is assessed using two methods: (1) the natural logarithm of the CEO’s signature area, measured via ImageJ software, and (2) a psychological index based on signature characteristics—such as complexity, presence of vertical lines, inclusion of the CEO’s name, and counterclockwise orientation—scored on a scale from 0 to 4. Managerial ability is evaluated using the Demerjian DEA-based model, where inputs include cost of goods sold (COGS), selling, general and administrative expenses (SG&A), fixed assets, and intangible assets, with sales serving as the output. The efficiency score derived from this model is then regressed on firm-specific variables, and the residuals are used as a proxy for managerial ability. The cost of debt (COD) is calculated by dividing interest expense by total liabilities. Panel data regressions with robust standard errors are employed, controlling for firm size, leverage, profitability, board independence, CEO tenure, CEO duality, and both industry and year fixed effects. Findings The results indicate that managerial narcissism is associated with an increase in the cost of debt, whereas managerial ability has a mitigating effect, leading to lower debt costs. However, the interaction between narcissism and managerial ability is not statistically significant when narcissism is measured by signature size. In contrast, when narcissism is assessed using a psychological signature index, the interaction becomes significant, suggesting that managerial ability can offset the adverse effects of narcissism on the cost of debt. Furthermore, when managers are categorized into distinct profiles—capable non-narcissists, incapable narcissists, and others—capable non-narcissistic managers emerge as the most effective in minimizing debt costs. Additional analyses reveal a U-shaped nonlinear relationship between managerial narcissism and the cost of debt, while managerial ability maintains a consistently negative linear association with debt costs. Discussion and Conclusion The findings highlight the complex and sometimes opposing roles of managerial traits in shaping corporate financing outcomes. While narcissism is often viewed as detrimental, it may offer certain advantages at moderate levels by fostering confidence and promoting innovation. However, excessive narcissism amplifies risk-taking and agency conflicts, ultimately leading to higher debt costs. In contrast, managerial ability consistently mitigates financial risk through enhanced decision-making and greater transparency. The significant interaction between the two traits suggests that managerial ability can buffer the negative effects of narcissism. The use of a psychological index to measure narcissism adds a nuanced perspective, uncovering associations that conventional metrics may overlook. These insights underscore the importance for policymakers, investors, and creditors to consider both psychological and competence-based evaluations of executives when assessing corporate risk and governance quality. Overall, the study contributes to the literature on behavioral finance and managerial decision-making in emerging markets, emphasizing the utility of multidimensional executive profiling in financial analysis.

https://amf.ui.ac.ir/article_29737_e82769d468072e5fd730cad2beb65189.pdf

University of Isfahan Journal of Asset Management and Financing 2383-1189 14 2 2026 06 22 Pricing the Default Risk Factor in Short-Term Debt: A Compound Option Approach in the Iranian Capital Market Pricing the Default Risk Factor in Short-Term Debt: A Compound Option Approach in the Iranian Capital Market 109 134 29780 10.22108/amf.2025.145501.1993 FA Mahnaz Khorasani Ph.D. Candidate, Department of Industrial Management, Faculty of Economics, Management and Administrative Sciences, Semnan University, Semnan, Iran Gholamhossein Golarzi Associate Professor, Department of Business Management, Faculty of Economics, Management and Administrative Sciences, Semnan University, Semnan Iran. Seyed Kazem Ebrahimi Associate Professor, Department of Accounting, Faculty of Economics, Management and Administrative Sciences, Semnan University, Semnan, Iran 0000-0002-7227-6407 Journal Article 2025 06 01 This study introduces and integrates short-term debt default risk as a novel systematic factor into the capital asset pricing framework and evaluates its impact on the explanatory power of existing multi-factor models in the Iranian capital market. Employing the structural Geske model—a compound option pricing approach—we estimate the default probabilities of short-term debt for firms listed on the Tehran Stock Exchange and Iran Fara Bourse between 2004 and 2023. These probabilities, derived through numerical solutions of nonlinear equation systems, serve as the basis for constructing a default risk factor, which is then incorporated into standard multi-factor asset pricing models. Time-series regressions were performed on test portfolios sorted by short-term default probability, as well as on control portfolios constructed without this characteristic for robustness. The results demonstrate that the inclusion of the short-term default risk factor significantly enhances the explanatory power of asset pricing models across both portfolio types, underscoring its relevance as a priced risk factor in the Iran`s capital market. Keywords: Asset Pricing Factor Models, Compound Option, Short-Term Debt Default Risk, Stock Returns JEL Classification: C22, C51, G12, G33 Introduction Short-term debt default risk—the probability that a firm will fail to meet its immediate financial obligations—has garnered increasing scholarly interest, particularly in financial systems where firms exhibit high dependence on short-term borrowing and face persistent refinancing requirements (Corvino & Fusai, 2022; Li & Sun, 2023). This form of risk is acutely heightened during periods of liquidity stress, amplifying corporate financial fragility. A body of empirical research across both developed and emerging markets—including the U.S., China, Europe, and Australia—affirms that default risk is a significant determinant of equity pricing (Chen & Hill, 2013; Li & Sun, 2023; Yang & Hu, 2024). Nevertheless, conventional asset pricing models have predominantly neglected to incorporate short-term debt default risk as an explicit, standalone risk factor (Li & Lin, 2021). This oversight is particularly consequential in the context of the Iranian capital market, where short-term instruments comprise a substantial share of corporate financing structures. To address this gap, this study investigates the hitherto unexplored role of short-term debt default risk in explaining stock returns within this market. Employing the structural Geske (1977) model—an extension of the Merton (1974) framework based on compound option pricing—we estimate a novel risk factor proxying for the probability of short-term default. This factor is subsequently integrated into three established asset pricing models: the Capital Asset Pricing Model (CAPM) of Sharpe (1964) and Lintner (1965), the q-factor model by Hou et al. (2015), and the six-factor model of Fama and French (2018). The central aim of this study is to determine whether the inclusion of this default risk factor significantly enhances the explanatory power of these benchmark models. By constructing a theoretically grounded and empirically tested risk factor, this study contributes to the asset pricing literature and offers insights of practical relevance to both investors and policymakers operating in the Iran`s capital market. Materials and Methods This study employs an applied, ex post facto research design to examine the effect of short-term debt default risk on the explanatory power of asset pricing models. The population consists of all firms listed on the Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB) from 2004 to 2023. The sample was filtered according to criteria established in seminal asset pricing studies, including Fama and French (1993, 2015, 2018), Ball, Gerakos, Linnainmaa, and Nikolaev (2016), and Li and Lin (2021). Exclusions encompassed firms in the over-the-counter (OTC) base market, financial institutions, entities with negative book value, those experiencing extended trading halts, and firms with insufficient data availability. The final sample comprised 335 firms. Data were collected from the Tehran Securities Exchange Technology Management Company and Rahavard Novin software. Short-term debt default probabilities were estimated using the Geske (1977) model—a compound option extension of the Merton (1974) framework—by solving systems of nonlinear equations with multivariate normal distribution functions in MATLAB. Asset pricing model estimation and time-series regression analyses were conducted in Python. Factors constructed included the market risk premium (MRP), size (SMB), value (HML), profitability (RMW), investment (CMA), momentum (MOM), and the novel short-term default risk factor (STD). These factors were formed using the Fama and French (1993, 2015) independent 3×2 sorting methodology. Model performance was evaluated using time-series regressions on test portfolios sorted by short-term default probability, with robustness checks performed on control portfolios not sorted by this characteristic. Statistical significance was assessed using intercept (alpha) estimates and the Gibbons, Ross, and Shanken (1989) (GRS) test, consistent with the Fama and French empirical tradition. Findings The descriptive analysis indicates that the short-term debt default risk factor (PMD) carries a positive and statistically significant average return of 0.47%, consistent with the existence of a positive risk premium in the market. PMD demonstrates a positive correlation with the market factor and negative correlations with both profitability and investment factors. Time-series regression results reveal that the inclusion of the PMD factor significantly enhances the explanatory power of all asset pricing models examined. Specifically, augmenting the CAPM with PMD leads to a notable reduction in both the GRS statistic and the average absolute value of intercepts (A|αᵢ|), indicating a superior model fit. This improvement is further corroborated by declines in the metrics A|αᵢ| / A|r̄ᵢ| and A|αᵢ²| / A|r̄ᵢ²|, which signify a reduction in the proportion of cross-sectional return dispersion and variance that remains unexplained by the model. Parallel enhancements in model performance were observed when PMD was integrated into both the q-factor and the Fama-French six-factor (FF6) models. Across all augmented specifications (CAPM+PMD, q+PMD, FF6+PMD), the models consistently outperformed their original counterparts. This superior performance was robust across both double-sorted (5×5) and triple-sorted (2×4×4) portfolio formations. Crucially, robustness checks confirmed that these improvements are not an artifact of the sorting variable; even in test portfolios constructed without regard to default probability, the inclusion of PMD resulted in lower GRS statistics and diminished pricing errors. Discussion and Conclusion In conclusion, this study establishes that short-term debt default risk, a pivotal element of credit risk, is a significant determinant of stock returns, especially in markets characterized by a high reliance on short-term financing. While traditionally overlooked by mainstream asset pricing models, this omission potentially leads to biased estimates of expected returns. To address this gap, we developed a novel risk factor (PMD) grounded in the Geske (1977) compound option model and integrated it into three established asset pricing frameworks: the CAPM, the q-factor model, and the Fama-French six-factor model. Empirical analysis, conducted via time-series regressions on a sample of 335 firms from the Tehran Stock Exchange and Iran Fara Bourse (2004–2023), demonstrated that the inclusion of the PMD factor consistently and significantly enhanced the explanatory power of all models. This improvement was robust across test portfolios sorted by size, book-to-market, investment, profitability, and default probability, and was quantified by lower GRS statistics, reduced average absolute alphas, and decreased pricing error ratios. Crucially, the factor's efficacy extended to portfolios constructed without a default-risk characteristic, underscoring its role as a pervasive, non-diversifiable systematic risk factor within the Iranian capital market, rather than a mere idiosyncratic variable. This study introduces and integrates short-term debt default risk as a novel systematic factor into the capital asset pricing framework and evaluates its impact on the explanatory power of existing multi-factor models in the Iranian capital market. Employing the structural Geske model—a compound option pricing approach—we estimate the default probabilities of short-term debt for firms listed on the Tehran Stock Exchange and Iran Fara Bourse between 2004 and 2023. These probabilities, derived through numerical solutions of nonlinear equation systems, serve as the basis for constructing a default risk factor, which is then incorporated into standard multi-factor asset pricing models. Time-series regressions were performed on test portfolios sorted by short-term default probability, as well as on control portfolios constructed without this characteristic for robustness. The results demonstrate that the inclusion of the short-term default risk factor significantly enhances the explanatory power of asset pricing models across both portfolio types, underscoring its relevance as a priced risk factor in the Iran`s capital market. Keywords: Asset Pricing Factor Models, Compound Option, Short-Term Debt Default Risk, Stock Returns JEL Classification: C22, C51, G12, G33 Introduction Short-term debt default risk—the probability that a firm will fail to meet its immediate financial obligations—has garnered increasing scholarly interest, particularly in financial systems where firms exhibit high dependence on short-term borrowing and face persistent refinancing requirements (Corvino & Fusai, 2022; Li & Sun, 2023). This form of risk is acutely heightened during periods of liquidity stress, amplifying corporate financial fragility. A body of empirical research across both developed and emerging markets—including the U.S., China, Europe, and Australia—affirms that default risk is a significant determinant of equity pricing (Chen & Hill, 2013; Li & Sun, 2023; Yang & Hu, 2024). Nevertheless, conventional asset pricing models have predominantly neglected to incorporate short-term debt default risk as an explicit, standalone risk factor (Li & Lin, 2021). This oversight is particularly consequential in the context of the Iranian capital market, where short-term instruments comprise a substantial share of corporate financing structures. To address this gap, this study investigates the hitherto unexplored role of short-term debt default risk in explaining stock returns within this market. Employing the structural Geske (1977) model—an extension of the Merton (1974) framework based on compound option pricing—we estimate a novel risk factor proxying for the probability of short-term default. This factor is subsequently integrated into three established asset pricing models: the Capital Asset Pricing Model (CAPM) of Sharpe (1964) and Lintner (1965), the q-factor model by Hou et al. (2015), and the six-factor model of Fama and French (2018). The central aim of this study is to determine whether the inclusion of this default risk factor significantly enhances the explanatory power of these benchmark models. By constructing a theoretically grounded and empirically tested risk factor, this study contributes to the asset pricing literature and offers insights of practical relevance to both investors and policymakers operating in the Iran`s capital market. Materials and Methods This study employs an applied, ex post facto research design to examine the effect of short-term debt default risk on the explanatory power of asset pricing models. The population consists of all firms listed on the Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB) from 2004 to 2023. The sample was filtered according to criteria established in seminal asset pricing studies, including Fama and French (1993, 2015, 2018), Ball, Gerakos, Linnainmaa, and Nikolaev (2016), and Li and Lin (2021). Exclusions encompassed firms in the over-the-counter (OTC) base market, financial institutions, entities with negative book value, those experiencing extended trading halts, and firms with insufficient data availability. The final sample comprised 335 firms. Data were collected from the Tehran Securities Exchange Technology Management Company and Rahavard Novin software. Short-term debt default probabilities were estimated using the Geske (1977) model—a compound option extension of the Merton (1974) framework—by solving systems of nonlinear equations with multivariate normal distribution functions in MATLAB. Asset pricing model estimation and time-series regression analyses were conducted in Python. Factors constructed included the market risk premium (MRP), size (SMB), value (HML), profitability (RMW), investment (CMA), momentum (MOM), and the novel short-term default risk factor (STD). These factors were formed using the Fama and French (1993, 2015) independent 3×2 sorting methodology. Model performance was evaluated using time-series regressions on test portfolios sorted by short-term default probability, with robustness checks performed on control portfolios not sorted by this characteristic. Statistical significance was assessed using intercept (alpha) estimates and the Gibbons, Ross, and Shanken (1989) (GRS) test, consistent with the Fama and French empirical tradition. Findings The descriptive analysis indicates that the short-term debt default risk factor (PMD) carries a positive and statistically significant average return of 0.47%, consistent with the existence of a positive risk premium in the market. PMD demonstrates a positive correlation with the market factor and negative correlations with both profitability and investment factors. Time-series regression results reveal that the inclusion of the PMD factor significantly enhances the explanatory power of all asset pricing models examined. Specifically, augmenting the CAPM with PMD leads to a notable reduction in both the GRS statistic and the average absolute value of intercepts (A|αᵢ|), indicating a superior model fit. This improvement is further corroborated by declines in the metrics A|αᵢ| / A|r̄ᵢ| and A|αᵢ²| / A|r̄ᵢ²|, which signify a reduction in the proportion of cross-sectional return dispersion and variance that remains unexplained by the model. Parallel enhancements in model performance were observed when PMD was integrated into both the q-factor and the Fama-French six-factor (FF6) models. Across all augmented specifications (CAPM+PMD, q+PMD, FF6+PMD), the models consistently outperformed their original counterparts. This superior performance was robust across both double-sorted (5×5) and triple-sorted (2×4×4) portfolio formations. Crucially, robustness checks confirmed that these improvements are not an artifact of the sorting variable; even in test portfolios constructed without regard to default probability, the inclusion of PMD resulted in lower GRS statistics and diminished pricing errors. Discussion and Conclusion In conclusion, this study establishes that short-term debt default risk, a pivotal element of credit risk, is a significant determinant of stock returns, especially in markets characterized by a high reliance on short-term financing. While traditionally overlooked by mainstream asset pricing models, this omission potentially leads to biased estimates of expected returns. To address this gap, we developed a novel risk factor (PMD) grounded in the Geske (1977) compound option model and integrated it into three established asset pricing frameworks: the CAPM, the q-factor model, and the Fama-French six-factor model. Empirical analysis, conducted via time-series regressions on a sample of 335 firms from the Tehran Stock Exchange and Iran Fara Bourse (2004–2023), demonstrated that the inclusion of the PMD factor consistently and significantly enhanced the explanatory power of all models. This improvement was robust across test portfolios sorted by size, book-to-market, investment, profitability, and default probability, and was quantified by lower GRS statistics, reduced average absolute alphas, and decreased pricing error ratios. Crucially, the factor's efficacy extended to portfolios constructed without a default-risk characteristic, underscoring its role as a pervasive, non-diversifiable systematic risk factor within the Iranian capital market, rather than a mere idiosyncratic variable.

https://amf.ui.ac.ir/article_29780_c2ba65c821cade1b083e72405f4b3676.pdf