https://amf.ui.ac.ir/ Journal of Asset Management and Financing en daily 1 Mon, 22 Jun 2026 00:00:00 +0330 Mon, 22 Jun 2026 00:00:00 +0330 https://amf.ui.ac.ir/article_30347.html https://amf.ui.ac.ir/article_29331.html 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 IntroductionArtificial 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. MethodThis 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 IntervieweesNo.OrganizationPositionEducationGender1Capital MarketSenior ManagerMaster of AccountingMale2UniversityProfessorPh.D. in AccountingMale3UniversityProfessorPh.D. in Financial ManagementFemale4UniversityProfessorPh.D. in AccountingFemale5Capital MarketSpecialistMaster of FinanceMale6Capital MarketSpecialistMaster of AccountingMale7Capital MarketMiddle ManagerPh.D. in FinanceMale8UniversityProfessorPh.D. in AccountingMale9Capital MarketAccountantMaster of AccountingFemale10Capital MarketAccountantPh.D. in FinanceMale11UniversityProfessorPh.D. in AccountingMale12Capital MarketMiddle ManagerMaster of Financial SciencesMale 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. FindingsIn 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 ConclusionCorporate 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_29475.html 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_29701.html 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_29751.html 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. IntroductionAccess 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 & MethodsThis 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. FindingsThe 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 ConclusionThe 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_29737.html 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. IntroductionRecent 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 & MethodsThis 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. FindingsThe 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 ConclusionThe 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_29780.html 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 ReturnsJEL 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 MethodsThis 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. FindingsThe 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 ConclusionIn 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_29704.html  This study investigates the nonlinear relationship between bank capital and liquidity creation across the distribution of liquidity creation (by deciles) in both developed and developing countries. Recognizing the critical role of liquidity creation in fostering financial stability and economic growth, the analysis addresses how the magnitude and direction of bank capital’s impact may vary across different levels of liquidity creation and country types. Employing quantile regression on a sample of 59 developing and 37 developed countries from 2004 to 2023, the findings reveal a consistently negative and significant effect of bank capital on liquidity creation throughout all deciles. However, this negative effect attenuates in higher deciles for developed countries, whereas it intensifies in developing countries. Furthermore, economic growth, financial inclusion, and financial development indices generally exhibit positive and significant effects. Conversely, the financial stability index demonstrates a significant negative impact in the lower deciles for developed nations and the higher deciles for developing economies. These contrasting outcomes underscore fundamental differences in liquidity creation mechanisms across countries and emphasize the necessity of a disaggregated, context-specific approach to banking regulation and policy formulation.Keywords: Liquidity Creation, Bank Capital, Quantile Regression, Developing and Developed CountriesJEL Classification: G28, G21, C23 IntroductionLiquidity creation, a fundamental function of banks within the modern financial system, involves the transformation of short-term liabilities into long-term loans, a process inherently coupled with risk intermediation. Within this framework, bank capital serves a dual and potentially contradictory role: while it provides a crucial buffer against losses that can enhance a bank's capacity to assume risk and create liquidity, it may also constrain the resources available for lending. This tension suggests a nonlinear relationship, wherein the effect of capital on liquidity creation may vary across its distribution. At lower capital levels, increases might initially curb liquidity creation by reducing lendable funds, whereas at higher levels, the enhanced risk-absorbing capacity could facilitate greater liquidity creation.This non-uniformity implies that the impact of capital regulations is likely heterogeneous across institutions, challenging the efficacy of a one-size-fits-all regulatory approach. Motivated by this complexity, the present study employs a quantile regression (QR) methodology to empirically investigate the nuanced relationship between bank capital and liquidity creation across different deciles of the liquidity creation distribution. By analyzing a global sample of 37 developed and 59 developing countries over the period 2004–2023 within separate models, this research aims to provide a more disaggregated understanding critical for designing targeted prudential policies. Materials & MethodsThis study employs a panel quantile regression (QR) methodology to examine the nuanced impact of bank capital on liquidity creation across the entire conditional distribution of the latter. Selected for its capacity to provide a comprehensive analysis beyond the conditional mean, this approach is particularly advantageous for capturing potential heterogeneity in the relationship across different deciles, including the tails of the distribution. In contrast to conventional ordinary least squares (OLS) regression, QR estimates coefficients by minimizing a weighted sum of absolute deviations, known as the Least Absolute Deviation (LAD) method. This technique is robust to non-normal error distributions, heteroscedasticity, and the presence of outliers, thereby yielding more reliable and efficient estimates for our financial dataset, which may exhibit such characteristics.Aligned with this rationale, we estimate a nonlinear panel quantile regression model for a global sample comprising 37 developed and 59 developing countries over the period 2004–2023. The general empirical specification, adapted from the frameworks established by MazioudChaabouni et al. (2018) and Gupta et al. (2023), is formally defined as follows: (1)in which, LM represents liquidity creation, LCAR represents bank regulatory capital (transition variable), LBZSCORE is the financial stability, LGDPP is the variable of economic growth, LATM represents the financial inclusion index, and LFSD is the financial development index. FindingsThe stationarity of the variables was assessed using the Levin, Lin, and Chu (LLC) unit root test. As detailed in Table 1, the results confirm that all variables are stationary at the 5% significance level, incorporating an intercept term. Subsequently, the core empirical analysis, illustrated in Figure 5-a, reveals a statistically significant negative relationship between bank capital (LCAR) and liquidity creation (LM) across all deciles for developed countries. Notably, the magnitude of this negative effect exhibits a diminishing pattern, weakening progressively throughout the higher deciles of the liquidity creation distribution.  Chart (5-a). Trends in variables in deciles in developed countries Furthermore, the results for developing countries, as visualized in Figure 5-b, indicates a distinct and evolving relationship. The effect of bank capital (LCAR) on liquidity creation (LM) is statistically insignificant and positive in the first decile. However, this relationship transitions to a negative and statistically significant influence beginning in the second decile. Moreover, the magnitude of this adverse effect demonstrates a pronounced intensification across the higher deciles of the distribution.  Chart (5-b). Trends in variables in deciles in developing countries Conclusion and discussionIn conclusion, this study establishes that the relationship between bank capital and liquidity creation is not only nonlinear but also contingent upon a country's developmental context and its position within the liquidity creation distribution. The analysis reveals a consistently negative yet diminishing effect across deciles for developed nations, while in developing countries, the relationship manifests as negative and significant from the second decile onward, intensifying markedly at higher levels. This stark heterogeneity underscores fundamental differences in the operational mechanisms of financial intermediation and the distinct role capital plays across diverse economic landscapes. Furthermore, control variables corroborate this complexity; economic growth, financial inclusion, and financial development predominantly exert a positive influence on liquidity creation, whereas financial stability exhibits a significant negative impact in specific deciles, particularly within developing economies. Collectively, these findings carry substantial policy implications, strongly advocating for a disaggregated regulatory approach.Policymakers must therefore eschew uniform, one-size-fits-all capital regulations in favor of frameworks meticulously tailored to a country's level of financial development and the specific characteristics of its banking institutions. The application of quantile regression in this analysis proves indispensable, providing the granular insights necessary for such precise and effective policy formulation. https://amf.ui.ac.ir/article_29846.html The primary objective of this study is to design an Early Warning System (EWS) based on a Long Short-Term Memory (LSTM) architecture for the timely forecasting of price bubbles in the Tehran Stock Exchange (TSE). A secondary objective is to compare the predictive performance of this model against a Logistic Regression (LR) benchmark, using evaluation metrics such as the AUC-ROC and confusion matrix. The system's performance was evaluated on five selected TSE indices. Utilizing monthly data from 2002 to 2023, bubble periods were identified via the Generalized Supremum Augmented Dickey-Fuller (GSADF) test and represented as a binary variable. This bubble variable was then modeled using price changes of key warning indicators. The proposed deep learning-based system achieved predictive accuracy ranging from 73% to 81%, with the highest performance on the Total Index (81%) and the lowest on the Basic Metals Index (73%). A comparative analysis demonstrates that the LSTM model outperformed the LR model across all selected indices. The evaluation metrics confirm the superior performance of the LSTM model. To the best of our knowledge, this study presents the first reported EWS designed for predicting price bubbles in this context using a deep learning approach. Keywords: Generalized Supremum Augmented Dickey-Fuller Test (GSADF), Early Warning System (EWS), Logistic Regression (LR), Long Short-Term Memory (LSTM) JEL Classification: G01, G32, C45, C53   Introduction The bursting of price bubbles can precipitate financial crises, rendering the study of bubbles and market collapses highly consequential for investment portfolio risk management (Kaliva & Koskinen, 2008). The early identification of such bubbles and the forecasting of their trajectories are therefore crucial for policymakers and market participants, as it enables preventive measures to mitigate or avert financial turmoil. Consequently, the development and deployment of financial EWS are essential to actively reduce economic vulnerabilities and counteract price bubbles (Claessens & Kose, 2013). As Phillips et al. (2015) note, an effective EWS must achieve a high degree of accurate detection to facilitate swift and effective policy implementation, while simultaneously maintaining a low false-positive rate to avoid unnecessary policy actions. Analyzing historical bubble episodes and the factors influencing their emergence is thus fundamental to informed decision-making and the control of market irregularities (Sadeghisharif et al., 2017). In this context, deep learning algorithms have significantly advanced machine-learning models by offering greater computational speed and predictive precision. The rapid evolution of this field has attracted considerable attention from economists addressing a range of problems, particularly in the domain of asset price forecasting (Khaliliaraghi et al., 2022). The primary objective of this study is to evaluate bubble periods in selected Tehran Stock Exchange (TSE) indices and, by incorporating price changes from a set of early warning indicators, to design a bubble prediction system using an LSTM model. This research further aims to compare the predictive accuracy and quality of the LSTM model against an LR benchmark. Accordingly, this research seeks to answer the following question: Does the employment of a deep learning approach improve the accuracy and quality of bubble forecasts across all selected indices in this study?   Materials and Methods The target variables in this study were identified through a comparative analysis. Five TSE indices were selected: the Total Index, the 50 Most Active Companies Index, the Industry Index, the Financial Index, and the Basic Metals Index. Monthly data for these indices were collected for the period from 2002 to 2023.  Bubble episodes were identified using the GSADF test, and the resulting bubble signals were used to construct a dummy bubble variable, which served as the dependent variable in the subsequent modeling. Based on a literature review, eighteen influential variables were drawn from macroeconomic indicators, market information, valuation multiples, and commodity prices to serve as independent warning indicators. The returns for these variables were calculated as logarithmic changes. Given the time-series nature of the data, these indicators were structured with a 5-period lag and normalized to serve as input features for the models. The contribution of these variables to predictive power, while indirect, is evidenced through the final model performance. Subsequently, bubble-forecasting models were developed and an EWS was designed using a Recurrent Neural Network with an LSTM architecture as the deep learning approach, alongside an LR model as a classical machine-learning benchmark. Finally, model performance was evaluated based on the model type and stock index, using forecasting accuracy, the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and the confusion matrix. This evaluation framework allowed for a comprehensive assessment and comparison of the models' predictive capabilities.   Findings As illustrated in Table 1, the highest forecasting accuracy for the LR model was achieved by the Financial Index at 79%, while the lowest was observed for the Basic Metals Index at 65%. The AUC evaluation for the LR model indicates its strongest performance on the Financial Index (92%) and its weakest on the Basic Metals Index (79%). Furthermore, the confusion matrix assessment for the LR model reveals notably low true positive rates for the test data, ranging from 0% to 17%. According to the results in Table 2, the LSTM model attained its highest forecasting accuracy on the Total Index (81%) and its lowest on the Basic Metals Index (73%). A comparative analysis of predictive accuracy demonstrates that the LSTM model outperformed the LR benchmark across all indices. The AUC evaluation for the LSTM model also shows its best performance on the Financial Index (92%) and its lowest on the Basic Metals Index (81%). The confusion matrix for the LSTM model indicates substantially higher true positive rates, ranging from 38% to 71%, with the highest rate for the Total Index (71%) and the lowest for the 50 Most Active Companies Index (38%).   Table (1) Evaluation of the accuracy of the LR model for training and testing datasets by index Testing Training Indices 0.69 0.83 Total 0.69 0.85 Industry 0.79 0.88 Financial 0.67 0.94  50 Most Active Companies 0.65 0.85 Basic Metals   Table (2) Evaluation of the accuracy of the LSTM model for training and testing datasets by Index Testing Training Indices 0.81 0.85 Total 0.77 0.88 Industry 0.80 0.84 Financial 0.75 0.94  50 Most Active Companies 0.73 0.87 Basic Metals   Discussion and conclusion The results demonstrate that the proposed EWS achieves predictive accuracy ranging from 73% to 81% across all selected indices, with the highest performance on the Total Index (81%) and the lowest on the Basic Metals Index (73%). A comparative analysis of model performance confirms that the LSTM model consistently outperforms the LR benchmark across all indices. This superiority is particularly evident in the case of the Total Index, where the LSTM model's accuracy of 81% represents a 12-percentage-point improvement over the LR model's 69%. Similarly, the accuracy improved by 8 percentage points for the Industrial, 50 Most Active Companies, and Basic Metals indices, while a marginal improvement of 1 percentage point was observed for the Financial Index. In terms of predictive quality, as evaluated by the AUC-ROC and confusion matrices, the findings present a nuanced picture. The AUC-ROC metrics indicate that the performance of the LSTM and LR models is somewhat comparable. In contrast, the evaluation based on confusion matrices reveals a substantially superior performance for the LSTM model, particularly in its ability to correctly identify true positives.   https://amf.ui.ac.ir/article_29834.html This study explores the transmission of risk from key macroeconomic variables, specifically the exchange rate, oil price, inflation, and liquidity, to Sukuk returns, employing the Time-Varying Parameter Vector Autoregression (TVP-VAR) model over the period 2014 to 2022. The findings indicate that these variables play distinct roles in the spillover of risk to Sukuk instruments. Exchange rate and oil price are identified as the primary transmitters of risk, exerting a persistent influence on Sukuk returns, particularly those of non-governmental Sukuk. Inflation also demonstrates a significant impact, underscoring its critical role in the risk transmission process. Across all time periods, both governmental and non-governmental Sukuk are consistently characterized as recipients of risk from macroeconomic shocks. Short-term over-the-counter (OTC) Sukuk are especially susceptible to fluctuations in exchange rate and inflation. These results suggest that policymakers should prioritize the stabilization of the exchange rate and oil price volatility to mitigate their adverse effects on financial markets. Furthermore, the study corroborates previous research, reaffirming the strong influence of exchange rate and oil price dynamics on Sukuk performance across different timeframes. In light of these findings, economic strategies targeting these macroeconomic variables should be given heightened attention within national financial and economic policy frameworks to reduce investment risk in Sukuk and enhance resilience to economic shocks. Keywords: Sukuk, Exchange Rate, Oil Prices, Inflation, TVP-VAR. JEL Classification: E44, P44, C58   Introduction Financial markets are a core pillar of national economies, playing a crucial role in resource allocation and asset pricing. In conventional financial markets, bond issuance serves as a primary mechanism for financing. Since traditional bonds are based on interest-bearing loans, they are considered usury and are prohibited in Islam. Consequently, these instruments cannot be used for financing in Islamic economies. In response, Islamic countries, drawing on the knowledge of scholars, have issued Sharia-compliant securities to support interest-free banking. Islamic financial instruments, particularly Sukuk, have attracted substantial interest from a broad range of investors. Given the increasing prominence of Sukuk in Iran’s financial market, the yields on these securities are influenced by a set of macroeconomic factors. Variables such as exchange rates, oil prices, liquidity, and inflation can affect yield levels and the risk structure of these securities through various channels (Umar et al., 2023). The importance of this issue extends beyond yield fluctuations; shocks and volatility in these macroeconomic variables can be transmitted to the sukuk market, altering its risk dynamics. In the financial literature, this process is referred to as risk transmission and can exhibit asymmetry, dynamics, and time-varying behavior (Billah et al., 2022; Samitas et al., 2021). Thus, the research hypotheses specify the intensity of risk transmission from each macroeconomic factor, namely, the percentage changes in exchange rates, oil prices, liquidity, and inflation, to Sukuk returns. Methods and Materials This study investigates risk transmission and its intensity between macroeconomic factors, specifically, the percentage changes in oil price, exchange rate, liquidity, and inflation, and sukuk returns, including total Farabourse sukuk, Farabourse government bonds, Farabourse non-government bonds, and Farabourse short-term bonds. Monthly data for the macroeconomic variables for the period 2014–2022 were obtained from the Central Bank website. Sukuk returns comprise two components: price return and coupon (interest) return. The price return reflects changes in the market price of the sukuk, while the coupon return corresponds to the interest portion of the debt instrument since the last coupon payment. Information regarding debt security indices was extracted from the Iran Fara Bourse website. This study utilizes the methodology introduced by Balcilar et al. (2021), which is an improved version of the approach developed by Antonakakis et al. (2020). The extended connectedness framework proposed by Balcilar et al. (2021) offers several key advantages over the previous method (Antonakakis et al., 2020). In addition to capturing dynamic interconnections, this framework allows for a more precise analysis of net directional linkages within the connectedness structure. While Anton's approach (Antonakakis et al., 2020) relies on fixed parameters and a general framework for dynamic connectedness analysis, the technique developed by Balcilar et al. (2021) yields more accurate and flexible results with less sensitivity to outliers. These features make it a more robust tool for identifying shock transmission in complex financial and economic networks.   Findings The findings indicate that the oil variable emerges as the primary risk transmitter within the entire network, encompassing all variables considered in the study. The total return of sukuk throughout the study period has acted as a risk receiver from oil. Oil transmitted risk to non-governmental sukuk with high intensity over a short interval. The transmission of risk from oil to short-term sukuk remained stable and constant across the entire study period, without notable changes. The intensity of risk transmission from oil to governmental sukuk was moderate for roughly half of the study period, with a brief period characterized by an increased intensity of transmission. The exchange rate stands as one of the most influential nodes for risk transfer within the network; while it is influenced by oil, it also propagates substantial risk to other variables, including liquidity, inflation, and, in particular, the sukuk market. Liquidity plays a dual role: it is influenced by oil, the exchange rate, and inflation, yet it also acts as a key conduit for transferring risk to Sukuk. The transmission of risk from liquidity to the total return of Sukuk persisted across the entire study period. In most subperiods, the transfer of risk from liquidity to total Sukuk and to non-governmental Sukuk remained stable. The short-term Sukuk segment experienced the greatest impact from liquidity, but only during a brief interval. Government Sukuk exhibited the highest sensitivity to liquidity in a short period; however, in most periods examined, this susceptibility declined. Inflation is typically viewed as a recipient of risk from oil and the exchange rate; however, it has played a more active role with respect to Sukuk. In most periods studied, the risk transfer from inflation to the total return of Sukuk remained stable. The risk transfer from inflation to the return of non-governmental Sukuk was stable for a short period but exhibited high intensity for most of the study horizon. In the short term, the risk transfer from inflation to short-term Sukuk peaked, while in the medium term, it trended downward. From the medium term onward, a pronounced decline in the risk transfer from inflation to governmental Sukuk is observed.   Discussion and Conclusion Based on the findings regarding the impact of macroeconomic factors on sukuk returns, the government, to improve its financing conditions, should foster stability in the sukuk market and reduce investor risk within this market. The same applies to corporate financing. The Central Bank, through appropriate monetary policy measures, should aim to stabilize macroeconomic variables such as inflation and exchange rates, to reduce the volatility of sukuk returns. Given the findings on risk transmission from oil price changes to sukuk returns and the country’s reliance on oil revenues, policymakers are advised to pursue diversification of government revenue sources beyond oil, for example, through tax instruments, to enhance stability in the sukuk market. Enhancing transparency in fiscal and monetary policies and disseminating accurate information can help investors better anticipate developments and reduce uncertainty, thereby contributing to lower sukuk return volatility. https://amf.ui.ac.ir/article_29900.html Firms experiencing financial constraints, often a consequence of weak performance, may engage in tax avoidance to alleviate funding pressures. While this practice can mitigate financial strain, it may also create opportunities for managers to pursue self-interested objectives, such as managerial empire-building, at the expense of shareholder wealth maximization. This study examines the effect of tax avoidance on managerial empire-building and investigates the moderating role of financial constraints in this relationship. Using panel data from 105 firms listed on the Tehran Stock Exchange over the period 2014–2022, we test our hypotheses using multiple regression analysis. The results indicate that tax avoidance has a positive and significant effect on empire-building. Moreover, financial constraints strengthen this relationship, implying that managers under greater funding pressure are more likely to divert tax savings toward opportunistic empire-building activities. These findings contribute to the corporate finance and governance literature by elucidating how a firm's financial environment conditions the consequences of tax avoidance. The study highlights the dual nature of tax avoidance: though it can serve as a source of internal financing, it may also enable value-destroying managerial behavior. Consequently, this research offers valuable insights for policymakers, shareholders, and boards of directors aiming to enhance oversight and ensure that tax savings are allocated to value-enhancing investments.Keywords: Tax Avoidance, Management Empire Building, Financial Constraints.JEL Classification: H26, G32, G01 IntroductionManagerial empire-building occurs when managers expand firms beyond their optimal size to pursue self-serving behavior, such as prestige, power, and excessive compensation (Bragoli, 2021; Hope & Thomas, 2008). This behavior often leads to overinvestment, inefficient asset growth, and value-destroying acquisitions. It can also diminish financial reporting quality, as managers may selectively conceal information to obscure the outcomes of such activities (Young et al., 2014; Weiskirchner-Merten, 2023).In parallel, tax avoidance is a prevalent corporate strategy aimed at reducing costs and enhancing liquidity (Pratama, 2018). While potentially increasing shareholder wealth in the short term, prior research suggests that in the absence of strong monitoring, managers may divert tax savings toward private gains, including empire-building (Desai & Dharmapala, 2006, 2009; Atwood & Lewellen, 2019). Evidence indicates that weak governance structures amplify these agency problems, ultimately eroding firm value (Hanlon & Heitzman, 2010; Shams et al., 2022; Sadeghi et al., 2023).Empirical studies further document a positive association between tax avoidance and the inefficient expansion of firm assets (Desai et al., 2007; Chen et al., 2010). More recent scholarship emphasizes the moderating role of financial constraints, positing that when external financing is limited, tax avoidance serves as an alternative internal funding channel, thereby exacerbating managerial opportunism (Dhaliwal et al., 2004; Edwards et al., 2013).Building on this theoretical foundation, the present study tests the following hypotheses: 1) Tax avoidance has a positive effect on managerial empire-building and 2) Financial constraints strengthen the positive relationship between tax avoidance and managerial empire-building.The findings are expected to offer valuable insights for shareholders, regulators, and policymakers by elucidating how the interplay between tax avoidance and empire-building distorts corporate resource allocation and potentially undermines broader stakeholder interests.Materials & MethodsThe analysis utilizes panel data from companies listed on the Tehran Stock Exchange (TSE) over the period 2014-2015. The initial sample was subjected to standard screening procedures, resulting in a final balanced panel of 105 firms. All data were processed and analyzed using EViews 10. The dependent variable, managerial empire-building, is measured using a composite index constructed from five components—acquisitions, consolidations, capital expenditure growth, total asset growth, and tangible fixed asset growth—this index follows the methodologies established in prior literature (Chhaochharia et al., 2012; Levi et al., 2014; Gul et al., 2020; Shams et al., 2022). The index is calculated according to Equation (1), which normalizes the value to a range between zero and one.        (1) In this index, the numerator represents the count of conditions satisfied by the firm (each coded as 1), and the denominator is the total number of conditions (five), yielding a normalized score ranging from 0 to 1. The independent variable, tax avoidance (TAX_AVOID), is measured as the ratio of cash tax payments to pre-tax book income, multiplied by –1 (Safari Graili & Pudine, 2016; Lee & Bose, 2021). This measure, often referred to as the cash effective tax rate (ETR), results in higher values indicating a greater degree of tax avoidance.Financial constraints (FC) are measured using the Z-score model developed by BadavarNahandi and Darkhor (2013), specified as follows:  (2)This version is efficient and commonly used in high-impact papers.The model incorporates several control variables to mitigate omitted variable bias, including cash holdings, leverage, profitability (return on assets, ROA), firm size, market-to-book ratio, sales growth, firm age, institutional ownership, and CEO ability. CEO ability is estimated using the methodology developed by Demerjian et al. (2012). To test the first hypothesis, the following regression model is estimated:  (3)To test the second hypothesis, the interaction term TAX_AVOID × Z-score is added:   (4)where a positive and significant β3​ indicates that financial constraints strengthen the effect of tax avoidance on managerial empire-building. FindingsThe descriptive statistics indicate a mean tax avoidance value of –0.0831, suggesting the data are concentrated around this point. Diagnostic tests confirmed the presence of autocorrelation and heteroskedasticity; consequently, the models were estimated using AR(1) and Generalized Least Squares (GLS) methods to correct for these issues. The results indicate that tax avoidance exerts a positive and statistically significant effect on managerial empire-building (β = 0.224, p = 0.009) and supports the firs hypothesis. Among the control variables, cash holdings, financial leverage, profitability, firm size, and sales growth also showed positive and significant associations with empire-building. In contrast, the market-to-book ratio and firm age were negatively and significantly related to the dependent variable. The effects of institutional ownership and CEO ability were found to be statistically insignificant. The model demonstrates strong explanatory power, with an R-squared of 0.64.Regarding the second hypothesis, the analysis reveals that the interaction term between tax avoidance and financial constraints has a positive and significant effect on managerial empire-building (β = 0.234, p = 0.023). This finding suggests that financial constraints amplify the positive effect of tax avoidance on empire-building. Furthermore, a Wald test confirms that the coefficients for the main effect of tax avoidance and the interaction term are jointly significant and statistically distinct from one another (p = 0.045), thereby validating the moderating role of financial constraints. In summary, the results underscore that tax avoidance, by providing internal financial resources, strengthens managers' propensity for empire-building. This relationship is significantly intensified when firms face financial constraints.Discussion and conclusionThis study provides empirical evidence that tax avoidance has a positive and significant effect on managerial empire-building, thereby confirming its first hypothesis. This finding aligns with prior research (Sadeghi et al., 2023; Shams et al., 2022) and is well-explained by agency theory. The theory posits that the separation of ownership and control creates opportunities for managers to act in their own self-interest. In this context, tax avoidance serves as a mechanism to generate discretionary resources, which managers may then divert to pursue personal benefits—such as increased compensation, power, and prestige—through empire-building, rather than maximizing shareholder wealth. Furthermore, the results demonstrate that financial constraints significantly strengthen the positive relationship between tax avoidance and empire-building, thus supporting the second hypothesis. This indicates that the pressure of limited external financing exacerbates managerial opportunism, a finding consistent with extant empirical literature.These findings yield several important implications for corporate governance and investment. To mitigate these agency costs, shareholders and boards of directors should enhance monitoring mechanisms and redesign executive compensation contracts to better align managerial incentives with long-term value creation. This could involve appointing independent board members and, in egregious cases, replacing CEOs who persistently engage in value-destroying expansion. For investors, these results underscore the need for vigilant scrutiny of managerial behavior, particularly in firms with weak governance structures. When assessing corporate strategy, investors should distinguish between diversifications that create genuine synergies and those that merely reflect empire-building. In the latter case, where diversification is unrelated and value-destroying, divestiture may be a preferable strategy, as shareholders can achieve diversification more efficiently through their own portfolio choices. https://amf.ui.ac.ir/article_29940.html This study investigates the impact of investor sentiment on stock returns across short-term daily, weekly, and monthly horizons—namely the sentiment term structure. While prior literature establishes a link between sentiment and stock price volatility, empirical evidence on the role of the time horizon remains limited and conflicting. We test the effect of investor sentiment on excess stock returns at these three frequencies using panel regression, controlling for market excess returns, size, and value factors. Investor sentiment is measured through four indirect proxies, synthesized into composite indices using both Principal Component Analysis (PCA) and the Kalman filter. The results indicate that investor sentiment exerts a significant yet temporally decaying influence on stock returns; the magnitude of the effect diminishes from the daily to the weekly and monthly horizons, revealing a downward-sloping term structure. This finding is robust, as it holds for both the PCA- and Kalman filter-based sentiment indices. To our knowledge, this is the first study to systematically examine the differential impact of investor sentiment across these high-frequency horizons and the first to empirically validate its temporal structure using both of these methodological approaches. Key words: Kalman Filter, Principal Component Analysis, Stock Return, Term Structure of Investors Sentiment JEL Classification: C22, C38, G12, G41 Introduction Classical finance theories are predicated on the assumption of rational investors, asserting that stock prices are determined by fundamental factors while largely neglecting the influence of investor sentiment. However, this paradigm is challenged by historical market anomalies such as the 1929 Great Depression, Black Monday in 1987, and the dot-com bubble of the 1990s. Corroborating these events, a growing body of empirical research confirms that investor sentiment significantly contributes to stock price volatility (Baker & Wurgler, 2007; Kim & Ha, 2010; Kumar & Lee, 2006; Frazzini & Lamont, 2008; Antoniou et al., 2013). Despite this established connection, the majority of empirical studies examine sentiment effects within a single, static time horizon. A critical gap exists, as emerging evidence suggests that the impact of sentiment is not uniform but varies across different timeframes (Li, 2020; Kim & Ryu, 2021). The underlying rationale is that as new information emerges over weekly or monthly periods, initial emotional reactions among investors are corrected, and stock prices tend to revert toward their intrinsic values, highlighting the time-dependent nature of sentiment effects. This study, therefore, investigates the impact of investor sentiment on stock returns across daily, weekly, and monthly horizons—a relationship we term the temporal structure of sentiment. Elucidating this dynamic is crucial for informing sound investment decisions, effective policy-making, and robust risk management practices. To accurately measure this latent construct, we rely on indirect proxies derived from financial and economic variables. Recognizing that any single variable captures both sentiment and unrelated noise, we synthesize multiple indicators to construct a more efficient and robust measure of unobservable investor sentiment (Baker & Wurgler, 2006). Specifically, this study employs two distinct methodologies to create composite sentiment indices: Principal Component Analysis (PCA), which extracts the common variation from a set of proxies (Berger & Turtle, 2011; Huang et al., 2014; Kamath et al., 2024), and the Kalman filter, a state-space technique designed to process all available information from the variables while optimally minimizing noise and estimation errors.   Materials and Methods To test the first hypothesis concerning the impact of investor sentiment on excess stock returns across daily, weekly, and monthly horizons, a panel regression was employed, following Li (2020). Equations (1) through (3) were specified for this purpose and were estimated separately for the sentiment indices derived from Principal Component Analysis (PCA) and the Kalman filter.   (1)                                                                          (2)                              (3)         where  is investor sentiment change, is the size factor,  is the book-to-market factor,  is risk-free rate, and is market return at time t and i refers to ith firm stock. To test the second hypothesis—that the influence of investor sentiment diminishes over longer time horizons—the sentiment coefficients (β) estimated from the panel regressions were annualized using Equation (4). These annualized coefficients were then systematically compared across the daily, weekly, and monthly frequencies. (4)     Where denotes the annual sentiment coefficient,  represents the sentiment coefficient corresponding to each frequency, and the constant 250 indicates the assumed number of trading days in a year. Stock return was calculated as the natural logarithm of the difference in adjusted closing prices, which account for dividends and stock splits. The market risk premium was defined as the difference between the market return and the risk-free rate, the latter proxied by Central Bank bond yields. The market return was computed as the log return of the Tehran Stock Exchange (TSE) index. The size (SMB) and the value factor (HML) factors were constructed following the methodology of Fama and French (1992). The composite sentiment index was constructed for daily, weekly, and monthly frequencies using four variables and two methods of PCA and Kalman filter. PCA extracts common components assumed to capture investor sentiment, while the Kalman filter processes all observed information to estimate sentiment while minimizing noise (Brown & Cliff, 2004; Li, 2020). Sentiment proxies are measured as below: a) Adjusted turnover rate – reflects changes in stock liquidity due to investor sentiment (Baker & Stein, 2004).   (5)     In which, Rit​ represents the return of stock i at time t, VOLit​ denotes the trading volume (measured in number of shares) for firm i at time t, and Shares Outstanding refers to the total shares outstanding for firm i at time t. b) Buy–sell imbalance – captures net retail demand for a stock at a given time. (6)     In which  denotes the purchase volume of stock i on day j during period t, and  represents the selling volume of stock i on day j during period t. c) Trading volume – higher traded value indicates elevated investor sentiment (Li & Yang, 2017).   (7)     In which represents the total number of shares of stock i traded during period t, and denotes the closing price of stock i at time t. d) Psychological line index – measures the proportion of positive trading days, indicating general market sentiment toward a stock.   (8)     In which  indicates the number of trading days during period t on which the closing price of stock i exceeds that of the previous day, and  represents the total number of trading days for stock i during period t. Findings To assess the impact of investor sentiment on excess stock returns, we estimated panel regressions using a sequence of models: a single-factor model (investor sentiment), a two-factor model (adding the market excess return), and a multi-factor model (further incorporating the size and value factors). The results for the PCA-based sentiment index are reported in Table 1.   Table (1) Results of the effect of investor sentiment (PCA) on excess returns Variable Frequency Daily Weekly Monthly a Intercept 0/0027*** 0/0084*** 0/0258*** Investor Sentiment 0/0099*** 0/0249*** 0/0548*** Adjusted R-squared 0/1536 0/2292 0/2485 Annual Coefficient of Investor Sentiment 9/7420 2/2722 0/9019 b Intercept 0/0014*** 0/0044*** 0/1070*** Investor Sentiment 0/0093*** 0/0224*** 0/0484*** Excess Market Return 0/7290*** 0/6420*** 0/6140*** Adjusted R-squared 0/2614 0/3340 0/3628 Annual Coefficient of Investor Sentiment 8/3086 1/9088 0/7674   c   Intercept 0/0010*** 0/0037*** 0/0048*** Investor Sentiment 0/0091*** 0/0223*** 0/0440*** Excess Market Return 0/8994*** 0/6906*** 0/7949*** Size Factor (SMB) 0/0486*** 0/2648*** 0/5311*** Value Factor (HML) 0/0270*** -0/0202** 0/0054 Adjusted R-squared 0/2925 0/3492 0/4286 Annual Coefficient of Investor Sentiment 7/8744 1/8951 0/6801   The results indicate a positive and statistically significant effect of investor sentiment on excess stock returns across all examined frequencies—daily, weekly, and monthly. Notably, the adjusted R² values exhibit an increasing trend from daily to monthly horizons, suggesting that the explanatory power of sentiment strengthens over longer timeframes. This finding is consistent with the literature, including Andleeb (2023) and McClure et al. (2004), which posits that investor sentiment exerts a more pronounced influence on short-term investment decisions, with this effect gradually dissipating as the investment horizon extends. Crucially, the persistence of a significant sentiment effect in our multi-factor models, which control for other systematic risks, indicates that its impact on returns is distinct and not subsumed by established risk factors. To ensure robustness, the analysis was replicated using the Kalman filter-based sentiment index. As summarized in Table 2, the results remain qualitatively unchanged, thereby reinforcing the primary conclusions.   Table 2: Effect of Investor Sentiment (Kalman Filter) on Excess Return Variable Frequency Daily Weekly Monthly a Intercept 0/0007*** 0/0033*** 0/0155*** Investor Sentiment 0/0023*** 0/0092*** 0/0236*** Adjusted R-squared 0/0531 0/1483 0/2119 Annual Coefficient of Investor Sentiment 0/7396 0/5549 0/3245 b Intercept 0/0002*** 0/0007*** 0/0033*** Investor Sentiment 0/0023*** 0/0087*** 0/0218*** Excess Market Return 0/5878*** 0/5929*** 0/6084*** Adjusted R-squared 0/1429 0/2550 0/3565 Annual Coefficient of Investor Sentiment 0/7396 0/5182 0/2967   c   Intercept 0/0000 0/0003* -0/0010 Investor Sentiment 0/0023*** 0/0085*** 0/0205*** Excess Market Return 0/7609*** 0/6395*** 0/7630*** Size Factor (SMB) 0/4387*** 0/2389*** 0/4806*** Value Factor (HML) 0/0315*** -0/0084 0/0051 Adjusted R-squared 0/1770 0/2709 0/4152 Annual Coefficient of Investor Sentiment 0/7396 0/5037 0/2770 Consistent with the primary results, the Kalman filter-based sentiment index also demonstrates a positive and statistically significant influence on stock return across all observed frequencies. However, a key finding emerges when examining the annualized sentiment coefficients: their magnitude displays a monotonic decline as the investment horizon extends from daily to monthly. This pattern indicates a decaying term structure for investor sentiment, where its pricing effect attenuates over longer periods. This result robustly confirms that the impact of investor sentiment on excess returns follows a consistently downward-sloping term structure.   Discussion and conclusion This study provides robust evidence of a direct and significant impact of investor sentiment on excess stock return across daily, weekly, and monthly investment horizons. These findings align with a growing body of literature that underscores the importance of sentiment in short-term price formation (e.g., Andleeb, 2023; Seok et al., 2018; Ryu et al., 2016; Dai, 2025). A central finding is the decaying influence of investor sentiment as the observation horizon extends from daily to monthly data, revealing a consistently downward-sloping term structure. This pattern suggests that irrational behavioral factors disproportionately drive short-term investment decisions, whereas their influence wanes over longer periods. The results corroborate the findings of Yang & Gao (2014) and extend the work of Li (2020) to the context of the Tehran Stock Exchange, demonstrating that short-term waves of investor optimism or pessimism are ultimately transient. Consequently, investor sentiment appears to be a key driver of short-term asset mispricing, generating excess returns that are subsequently corrected as prices converge toward their fundamental values over the long run. Crucially, this downward term structure is not an artifact of measurement, as it holds consistently for sentiment indices constructed using both Principal Component Analysis and the Kalman filter. This methodological robustness strongly confirms the time-dependent nature of sentiment effects, as theorized by Fu (2024). Furthermore, the persistent explanatory power of sentiment even after controlling for market, size, and value factors—a consistency also noted by Brown & Cliff (2004)—indicates that the informational content of investor sentiment captures dimensions of risk and return distinct from those in traditional asset-pricing models. https://amf.ui.ac.ir/article_29937.html This study examines the impact of financial statements readability on corporate credit financing, with a specific focus on the moderating role of product market competition. Employing a causal research design, the analysis uses a sample of 141 companies listed on the Tehran Stock Exchange over the 10-year period from 2014 to 2024. The findings indicate a significant positive relationship between financial statement readability and access to credit. Furthermore, product market competition is shown to negatively moderate this relationship. Specifically, heightened competition attenuates the positive effect of readability, thereby constraining firms' ability to secure credit financing. These results underscore how competitive market forces can limit financial flexibility, even for firms with transparent disclosures. Keywords: Readability of Financial Information, Credit Financing, Product Market Competition, Stock Exchange JEL Classification: G30, G38, G40   Introduction Competitiveness denotes a firm's capacity to maintain its market position, protect corporate assets, ensure returns on investment, and safeguard future employment. Given this scope, competition exerts a substantial influence on corporate activities and strategic decisions (Khoddadi & Rashidi Baghi, 2014). A principal metric for evaluating market competition, monopoly power, and industry structure is the degree of concentration. Market concentration describes the distribution of market share among firms within an industry, effectively indicating the extent to which a small number of firms dominate total industry output. Consequently, industries with fewer participants typically exhibit higher concentration levels. An analysis of firms listed on the Tehran Stock Exchange (TSE) confirms this pattern; in major sectors such as petrochemicals, steel, automotive manufacturing, and financial intermediation, a limited number of large companies control the majority of industry sales, resulting in highly concentrated market structures. As noted in prior research, these dominant firms achieve superior sales revenues compared to their industry peers, a direct outcome of their market control (Khoddadeh Shamloo & Gharsi, 2018). Porter (1990) contends that product market competition shapes managerial decisions and is a critical determinant of corporate profitability. The competitive literature further posits that intense competition serves as an incentive for managerial efficiency, as competitive markets swiftly discipline underperformance. Thus, product market competition functions as an external governance mechanism, monitoring management and mitigating agency costs (Khoshkar & Farghani, 2020). Competitiveness can also be defined as an economic entity's ability to maintain or increase its share in international markets. A firm's sales volume is a reflection of its market influence; as such, companies are driven to preserve and expand their market share. This often leads to enhanced service quality for stakeholders—including the quality of disclosed financial information. Such improvements attract the attention of stakeholders and creditors, thereby indirectly influencing access to credit-based financing (Li et al., 2024). The research gap addressed by this study arises from the scarcity of comprehensive research that examines these factors in concert. Previous studies have predominantly investigated the impact of financial report readability on investor and creditor decisions in isolation. Financial information readability—defined as the ease with which financial statements can be understood—enhances transparency, reduces information asymmetry, and lowers financing costs. Simultaneously, the intensity of product market competition can alter managerial incentives and significantly influence financial performance and resource acquisition. The complex interplay between information readability and competitive market conditions remains a notable theoretical void. Furthermore, the role of product market competition in determining financial resource access establishes a foundation for improving credit acquisition capacity. Intensified competition encourages optimal resource utilization and fosters financial reporting transparency, which may, in turn, amplify the effect of financial information readability on credit financing. However, existing literature has inadequately explored the moderating role of product market competition, particularly within emerging markets characterized by unique institutional features. These considerations motivate the present study to address a critical scientific and practical gap by concurrently examining the roles of financial information readability and product market competition in corporate financing processes.   Hypothesis 1: A significant relationship exists between financial information readability and access to credit-based financing.   Hypothesis 2: Product market competition moderates the relationship between financial information readability and access to credit-based financing.   Materials & Methods This study employs a dataset comprising firms listed on the Tehran Stock Exchange (TSE). The data were sourced from the CODAL and TSE official websites and analyzed using EViews 12 software. The sample includes all companies listed on the TSE. This sample was selected based on data accessibility, its direct relevance to the research context, and the availability of audited, reliable financial statements. Following the application of systematic exclusion criteria detailed in Table 1, the final sample consists of 141 companies observed over the 10-year period from 2014 to 2023, yielding a balanced panel of 1,410 firm-year observations.   Findings Descriptive statistics are reported for the panel of 141 sample companies over the 10-year period from 2013 to 2022 (1,410 firm-year observations). The mean value for financial leverage is 0.53. The values of this parameter for firm size and return on assets are 1.72 and 0.15, respectively, with their standard deviations suggesting the dispersion around these means. The minimum and maximum values reported for each variable delineate their observed ranges. The diagnostic tests, summarized in Table 3, confirm the presence of cross-sectional dependence and serial correlation in the initial models. To address these issues, the models were estimated using the Generalized Least Squares (EGLS) method in EViews 12, which employs a robust variance-covariance matrix to correct for heteroskedasticity. Furthermore, the inclusion of an AR(1) term in the final model specification successfully mitigated the problem of serial autocorrelation. A Chow test, significant at the 5 percent level, supported the use of a panel data approach. Subsequently, a Hausman test, also significant at the 5 percent level, indicated that the fixed effects estimator was more appropriate than the random effects estimator for the final analysis. Based on the results, the financial information readability variable exhibits a positive and significant relationship with credit financing, with a coefficient of 0.54 (p < 0.01). Therefore, the first hypothesis is supported at the 1 percent significance level. The model demonstrates a high explanatory power, with an R-squared of 0.91, indicating that the independent and control variables account for 91 percent of the variation in the dependent variable. Furthermore, all variance inflation factor (VIF) values are below 5, confirming that multicollinearity is not a concern. The overall model fit is confirmed by the F-statistic, which is significant at the 1 percent level. The results for the second hypothesis are as follows. The interaction term between financial information readability and product market competition has a negative and significant coefficient of -0.46 (p < 0.01), indicating a negative moderating effect on credit financing. Thus, the second hypothesis is also supported at the 1 percent level. This finding suggests that increased product market competition attenuates the positive effect of financial readability on access to credit; in other words, competition acts as a moderating variable that weakens the benefit of readable disclosures. Qualitatively, this may be attributed to the heightened risk and uncertainty inherent in competitive markets. Intense competition pressures profitability and liquidity, potentially increasing lenders' perceived risk and caution in extending credit, thereby overshadowing the transparency benefits of readable reports. The model's R-squared is 0.90, the Durbin-Watson statistic is 1.89, and the VIF statistics remain below 5, collectively indicating a well-specified model with a strong fit, as confirmed by the significant F-statistic.     Discussion and conclusion As mentioned before the primary objective of this study was to investigate the role of financial information readability in securing credit financing, with a specific emphasis on the moderating effect of product market competition. The novelty of this research lies in its integration of two significant domains—financial economics and industrial organization—which have seldom been examined in a simultaneous and interactive manner. By combining the concept of financial report readability, which underscores information transparency and comprehensibility, with the dynamics of product market competition, this study establishes a new framework for understanding corporate resource acquisition mechanisms. The focus on competition as a moderating variable constitutes the central innovation, demonstrating how market competition intensity can influence the relationship between financial information quality and access to credit. This approach contributes new knowledge to the field of corporate finance and offers insights for refining credit policies in emerging markets, thereby addressing a significant theoretical and practical gap. Specifically, this study breaks new ground by analyzing the interplay between financial readability and product market competition, factors that have previously been studied in isolation. By focusing on financial report readability as a mechanism for enhancing transparency and reducing information asymmetry, and by analyzing the moderating role of market competition, this study offers a novel perspective on the corporate financing process. Consequently, it addresses a critical void in the literature, highlighting the significant role of the interaction between information quality and market structure in improving firms' access to credit, particularly within emerging economies. The results from the first hypothesis confirm a significant positive relationship between financial information readability and trade credit financing. Specifically, when companies provide accurate, comprehensible, and unambiguous information in their financial statements, it serves as a positive signal to financial statement users. This signal assures creditors that the firm has not engaged in informational obfuscation and possesses a sound financial position capable of repaying its obligations, thereby increasing the company's access to trade credit. Thus, the clarity and lack of complexity in financial disclosures directly influence credit-based financing. These findings align with existing research in this domain, such as Li et al. (2024). In essence, transparent and understandable financial information acts as a credible signal to creditors, bolstering their confidence in the firm's financial health and repayment capacity. This, in turn, increases creditors' willingness to extend financial resources and ultimately facilitates more favorable trade credit conditions for firms. Therefore, improving the quality and transparency of financial disclosures is not merely a regulatory or ethical imperative but also an effective strategy for enhancing financing efficiency in competitive markets. The results from the second hypothesis indicate that product market competition significantly moderates the relationship between financial information readability and access to credit financing. The negative and significant interaction term reveals that heightened competition diminishes the positive effect of readability on credit access. In highly competitive industries, where numerous firms vie for market share, managers are compelled to offer greater benefits to stakeholders to capture a larger market segment. This intense rivalry for resources can create challenges in securing trade credit, leading to reduced access. These findings are partially consistent with prior work, such as Li et al. (2024). Specifically, under conditions of high market competition, firms face increased pressure on their financial resources as they strive to offer competitive terms to stakeholders. In such an environment, even high levels of financial transparency may be insufficient to ease credit constraints, as creditors perceive higher competitive risks and consequently adopt more cautious and stringent lending practices. This finding underscores the complexity of financing in competitive markets, indicating that access to credit is not solely a function of information quality but is also critically shaped by external market conditions.   https://amf.ui.ac.ir/article_30042.html Abstract Purpose: The primary objective of this study is to examine the impact of trade friction on the financialization of firms. Additionally, it investigates the moderating role of managerial self-interest in the relationship between trade friction and financialization, as well as the mediating effect of managerial short-sightedness. Method: Three hypotheses were formulated to achieve the mentioned objectives. A sample of 103 companies from the Tehran Stock Exchange and Iranian over-the-counter markets from 2013 to 2023 was used to test the hypotheses. Multiple linear regression with the Price-Winston approach was applied to test the hypotheses and estimate the model parameters. Results: The study found that trade friction has a direct and significant impact on corporate financialization. Additionally, managerial self-interest significantly influences the relationship between trade friction and financialization, strengthening this link. Furthermore, managerial short-sightedness, as a mediating variable, directly affects corporate financialization, leading to its increase. Innovation: Previous studies have focused on examining the impact of managerial personality traits on financial decision-making, market reactions to environmental changes, and the relationship between financialization and real investment. No research has yet investigated the effect of managerial characteristics on the relationship between trade friction and corporate financialization. This study stands out due to the moderating role of managerial self-interest and the mediating effect of managerial short-sightedness, shifting the research focus to managerial characteristics. https://amf.ui.ac.ir/article_30093.html Portfolio optimization is a key challenge in financial investment. This study evaluates the performance of a proposed stock selection model for the Tehran Stock Exchange, comparing it with the market index and the Markowitz approach using statistical tests. Portfolios were constructed using the Hierarchical Risk Parity (HRP) method and marginal asset selection, based on two different correlation matrices. These portfolios were rebalanced semiannually according to correlation coefficients and the composition of the Top 50 Active Companies Index. The results show that the HRP approach with asset selection generally outperforms both the index and the Markowitz model in terms of portfolio performance, observed across various market conditions, including both recessions and booms. Statistical tests confirm the significant superiority of this method over the Markowitz approach. Additionally, this research achieved effective diversification in the Tehran Stock Exchange with fewer assets compared to other methods. The main innovation lies in using two distinct correlation matrices and applying two novel methods for asset weighting and selection, offering practical insights for both individual and institutional investors. https://amf.ui.ac.ir/article_30112.html By challenging the Efficient Market Hypothesis, the behavioral finance perspective asserts that investors’ decision-making is not driven solely by rationality and the pursuit of profit maximization. Instead, behavioral biases, such as investor sentiment, play a significant role in influencing investment decisions and, consequently, stock prices and returns. Accordingly, this study investigates the impact of investor sentiment on the momentum of excess stock returns, both in the short and long term, within the context of the Tehran Stock Exchange. A screening method was employed to select the sample, resulting in the inclusion of 169 listed firms. These firms were analyzed using multivariate regression models with panel data spanning 2011 to 2022 for the first hypothesis, and 2011 to 2020 for the second and third hypotheses, while controlling for year and industry effects. The findings indicate that investor sentiment positively affects the momentum of excess returns in the short term but has a negative impact in the long term. Moreover, the results suggest that investor sentiment exerts a stronger influence on the momentum of excess returns for smaller firms compared to larger firms over the long term. https://amf.ui.ac.ir/article_30156.html The main objective of this study is to design and implement a portfolio optimization model based on the quantitative value allocation approach within the context of Iran’s capital market. This developmental-applied research was formulated in two phases, including the quantitative evaluation of companies’ values and multi-objective portfolio optimization. Data were collected from reputable library and field sources and analyzed using the epsilon-constraint algorithm. The results confirm the effectiveness of the proposed approach in enhancing investors’ decision-making processes. Findings indicated that portfolios formed based on fundamental and technical analyses achieved higher returns compared to randomly selected portfolios and the presented model effectively incorporated diverse investor preferences in optimal asset selection. By developing a two-phase quantitative framework based on value functions and multi-objective optimization, this study introduced an accurate and flexible model for optimal asset allocation in Iran’s capital market, enabling the simultaneous incorporation of fundamental and technical analysis in the stock selection process. https://amf.ui.ac.ir/article_30255.html Objectives: Market participants are increasingly interested in quantitative trading models and the smart application of data science. These approaches can enhance the effectiveness of trading strategies, such as pairs trading—a valuable investment method even during bear market. Method: This strategy involves two key steps: selecting two securities (a pair), and detecting an anomaly in price gap between them (trading). We evaluate two machine learning approaches to determine the most effective algorithm for each stage. To efficiently group related securities and uncover high-potential pairs—tasks that even skilled investors may find challenging—we employed two density-based clustering methods: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and OPTICS (Ordering Points To Identify the Clustering Structure). These algorithms help define the search space by identifying meaningful clusters of securities. To reduce the portfolio drawdowns while maximizing returns after detecting price divergences, we implemented two time series forecasting models: Long Short-Term Memory (LSTM) and LSTM Encoder-Decoder. Results: OPTICS outperforms DBSCAN, demonstrating greater efficiency with fewer variables, a higher Sharpe ratio, and an increased proportion of profitable pairs. The LSTM encoder-decoder outperformed the LSTM model, delivering higher returns, an improved Sharpe ratio, and fewer days of portfolio decline. Key Innovations: This study introduces a novel approach by leveraging machine learning models for both phases of the strategy while optimizing model selection. Additional distinctive features include the use of high-frequency intraday data (5-minute intervals) (all market stocks, not a specific stock or industry), and the focus on net returns after accounting for transaction costs. https://amf.ui.ac.ir/article_30339.html Objective: Sticky expectations remain constant despite receiving new information related to the organization's status. This effect seems to exist only in situations that benefit the manager. In other words, the manager is slow to adjust his decisions when receiving new information. The purpose of this study is to investigate the stickiness of information expectations based on capital structure adjustment speed. Method: In order to investigate the subject under study, a mixed data regression model was used in the period 2016 to 2023 and data from 120 companies listed on the Tehran Stock Exchange were collected and used to test the research hypotheses. Findings: The results of the study indicate that we witness stickiness of managers' expectations in the companies under study. Also, the results of the second hypothesis of the research indicate that the capital structure adjustment speed has a significant effect on the managers’ sticky expectations. Finally, the results of the third hypothesis indicate that there is a significant difference between the high and low classes of the capital structure adjustment speed in terms of the sticky expectations, and it can be stated that changes in the disclosure quality lead to changes in the sticky expectations. Conclusion: The managers' sticky expectations leads to a decrease in the tendency to adjust forecasts and respond appropriately to new news. In general, the information disclosure quality and the capital structure adjustment speed can also be important from the perspective of financial policymaking and the development of reporting standards. https://amf.ui.ac.ir/article_30340.html Organizational capital, as a key intangible asset, plays a fundamental role in creating competitive advantage and enhancing firm performance. Drawing on social learning theory and institutional imitation theory, firms tend to model their behavior on that of industry peers under conditions of competition and uncertainty. The purpose of this study is to examine the impact of peer organizational capital on a firm’s organizational capital, as well as the moderating role of industry competition. The sample consists of 155 firms listed on the Tehran Stock Exchange over the period 2012–2023. To test the hypotheses, panel data regression with year fixed effects is employed. The results for the first hypothesis indicate that peer organizational capital has a positive and significant effect on firms’ organizational capital. This finding suggests that, under uncertainty, managers imitate the successful practices of competitors to reduce risk and gain legitimacy. The findings for the second hypothesis reveal that industry competition strengthens the peer effect of organizational capital; specifically, firms operating in more competitive industries exhibit stronger responses to their peers’ organizational capital. Robustness tests further confirm the stability of the results. Overall, investment in organizational capital is not solely an internal firm decision but is also shaped by peer behavior and the intensity of industry competition.