دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 13 4 2025 12 22 51 سال سیزدهم، شماره چهارم، شماره پیاپی 51، زمستان1404 29717 FA Journal Article 2025 07 27 https://amf.ui.ac.ir/article_29717_14d5d1989f73644573e500e635179d1f.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 13 4 2025 12 22 Managers' Ability and Debt Structure: Evidence from Iran's Financial Reporting Environment توانایی مدیران و ساختار بدهی: شواهدی از محیط گزارشگری مالی ایران 1 16 29470 10.22108/amf.2025.143681.1944 FA عقیل آل حسون دانشجوی دکتری، گروه حسابداری، دانشکده علوم اداری و اقتصاد، دانشگاه اصفهان، اصفهان، ایران سید عباس هاشمی دانشیار، گروه حسابداری، دانشکده علوم اداری و اقتصاد، دانشگاه اصفهان، اصفهان، ایران نرگس حمیدیان استادیار، گروه حسابداری، دانشکده علوم اداری و اقتصاد، دانشگاه اصفهان، اصفهان، ایران Journal Article 2025 01 03 The financing policies implemented by managers play a pivotal role in risk management and shareholder wealth creation. Consequently, identifying the factors that influence managerial financing decisions is critically important. This study examines the impact of managerial ability on short-term debt usage, incorporating the moderating effects of financial constraints and financial reporting quality. The sample includes 100 firms listed on the Tehran Stock Exchange, selected through systematic elimination for the period 2012–2023. A multivariate regression model based on panel data analysis was employed to test the hypotheses. The findings demonstrate that managerial ability has a positive effect on debt maturity. Additionally, while financial constraints do not significantly moderate this relationship, financial reporting quality strengthens the influence of managerial ability on short-term debt utilization. Specifically, high-ability managers—equipped with superior business acumen and strong incentives to signal their competence—tend to employ greater short-term debt to mitigate information asymmetry and enhance their reputational capital. Keywords: Debt Structure, Managers' Ability, Financial Constraints, Financial Reporting Quality JEL Classification: M40, H63, D04, M41 Introduction Debt financing is a fundamental component of corporate capital structure, playing a crucial role in firm sustainability and growth. The composition of debt—particularly its maturity structure—serves as a key determinant of financial stability and long-term success. Consequently, decisions regarding debt structure are critical, as misjudgments can expose firms to financial distress or even bankruptcy. Prior research has examined various determinants of debt maturity structure, including macroeconomic and institutional factors such as financial and political environments, legal and tax systems, information asymmetry, and capital provider characteristics. Another stream of literature focuses on firm-specific influences, particularly managerial traits, given their significance in mitigating agency conflicts between shareholders and managers. Among these traits, managerial ability stands out as a pivotal factor shaping debt maturity decisions. Aligned with theoretical foundations, this study proposes the following hypotheses: H₁: Managerial ability positively influences debt maturity. H₂: Financial constraints attenuate the effect of managerial ability on debt maturity. H₃: Financial reporting quality amplifies the impact of managerial ability on debt maturity.Materials & Methods and data The study examines firms listed on the Tehran Stock Exchange (TSE) over the period 2012–2023. The sample was selected through systematic elimination to ensure data integrity and representativeness. To test the hypotheses, we employed panel regression analysis using the Generalized Least Squares (GLS) estimator, which accounts for heteroskedasticity and autocorrelation in the data. Managerial ability was operationalized following Demerjian et al. (2012), while financial reporting quality was measured using the Dechow and Dichev (2002) accruals quality model. Finding The empirical results demonstrate several key insights. As presented in Table 2, managerial ability exhibits a statistically significant positive relationship with firms' utilization of short-term debt. This finding aligns with theoretical expectations, as short-term debt instruments can serve as effective mechanisms to mitigate information asymmetry between managers and investors. Moreover, the preferential use of short-term debt may function as a positive market signal, conveying managers' confidence in the firm's near-term financial prospects. Table 3 reveals that financial constraints do not significantly moderate the relationship between managerial ability and debt maturity structure. This suggests that capable managers maintain their influence over financing decisions regardless of external financial limitations. Finally, Table 4 presents evidence that financial reporting quality strengthens the positive association between managerial ability and short-term debt usage. This amplification effect likely occurs because high-quality financial reporting enhances transparency, thereby increasing the credibility of managers' financing decisions. Discussion and Conclusion Corporate financing decisions are predominantly shaped by managerial discretion, with short-term debt instruments gaining increasing prominence over the past three decades. Our findings align with signaling theory, which posits that short-term debt issuance serves dual purposes: it reduces information asymmetry while simultaneously functioning as a positive market signal of managerial competence. Conversely, agency theory would predict an inverse relationship, suggesting that higher managerial ability might correlate with reduced short-term debt due to inherent agency conflicts in firms where managerial capabilities are less observable. The empirical evidence supports the signaling perspective, demonstrating that high-ability managers strategically utilize short-term debt to distinguish themselves from their less competent counterparts. This behavior stems from their superior capacity to assess market conditions and capitalize on favorable financing opportunities. Furthermore, our analysis reveals that managerial ability plays a particularly significant role in firms with higher reporting quality. In such organizations, which typically possess more robust project portfolios, short-term debt issuance serves as an additional quality indicator. High-ability managers in these firms are more inclined to employ short-term debt instruments, thereby reinforcing their reputation for financial acumen and strengthening market confidence. These findings contribute to the ongoing theoretical discourse by reconciling competing perspectives from signaling and agency theories. They also offer practical implications for corporate governance, suggesting that boards should consider managerial ability as a key factor in financing policy decisions, particularly in firms with transparent financial reporting environments. سیاست‌های تأمین مالی که مدیران اتخاذ می‌کنند، نقش مهمی در ریسک و ایجاد ثروت برای سهام‌داران دارد؛ بنابراین، شناخت عوامل مؤثر در تصمیم‌های تأمین مالی مدیران اهمیت بسیار دارد؛ ازاین‌رو هدف این پژوهش بررسی تأثیر توانایی مدیران بر استفاده از بدهی‌های کوتاه‌مدت باتوجه‌به نقش تعدیلگر محدودیت مالی و کیفیت گزارشگری مالی است. شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران ‌به‌عنوان جامعۀ آماری این پژوهش در نظر گرفته شده است و تعداد 100 شرکت در بازۀ زمانی 1391 لغایت 1401 با استفاده از روش غربالگری ‌به‌عنوان نمونه انتخاب شد. برای آزمون فرضیه‌های پژوهش از الگوی رگرسیون چندمتغیره مبتنی‌بر داده‌های ترکیبی و روش حداقل مربعات تعمیم‌یافته استفاده‌ شده است. یافته‌های پژوهش نشان داد که توانایی مدیران بر سررسید بدهی‌ها تأثیر مثبت دارد؛ علاوه‌براین، محدودیت مالی بر رابطۀ توانایی مدیران و سررسید بدهی تأثیر ندارد؛ درنهایت یافته‌ها نشان داد که در شرکت‌های دارای کیفیت گزارشگری بالاتر، نقش توانایی مدیران در استفاده از بدهی‌های کوتاه‌مدت پررنگ‌تر است. مدیران با توانایی بالا که دارای دانش تجاری برتر با انگیزه‌های قوی برای برقراری ارتباط با توانایی برتر خود هستند، تمایل دارند تا از نسبت بیشتری از بدهی‌های کوتاه‌مدت استفاده کنند؛ درنتیجه عدم تقارن اطلاعاتی را کاهش می‌دهند و شهرت خود را تقویت می‌کنند.

https://amf.ui.ac.ir/article_29470_152f784244549007471ddfcbb7adb43e.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 13 4 2025 12 22 The Consequence of External Part of Transparency for Performance and Disclosure of Social Responsibility پیامد بخش برون‌سازمانی شفافیت برای عملکرد و افشای مسئولیت اجتماعی 17 34 29407 10.22108/amf.2025.142610.1915 FA اسیل مجید عبدالنبی دانشجوی دکتری، گروه حسابداری، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران غلامرضا منصورفر دانشیار، گروه حسابداری، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران حمزه دیدار دانشیار، گروه حسابداری، دانشکده اقتصاد و مدیریت، دانشگاه ارومیه، ارومیه، ایران Journal Article 2024 08 27 External transparency extends beyond the quality of internal disclosures, which are primarily governed by laws and regulations. This dimension of transparency encompasses external requirements and pressures that compel managers to adhere to higher standards of information disclosure. This study examines the impact of external transparency on corporate social responsibility (CSR) performance and disclosure. This study analyzes data from 105 companies listed on the Tehran Stock Exchange between 2013 and 2022, using EViews and Stata software. The findings reveal that heightened external transparency enhances both CSR performance and disclosure. External transparency pressures foster greater corporate transparency, thereby improving CSR disclosures. Additionally, increased transparency mitigates information asymmetry and agency problems, aligning managerial objectives with corporate goals and ultimately enhancing CSR performance. This study contributes to the literature by demonstrating that external transparency serves as a robust predictor of CSR activities. Moreover, it highlights the role of external transparency in encouraging managers to produce more comprehensive CSR reports. The research also uncovers policy implications, illustrating how external transparency pressures drive firms toward greater social responsibility. Keywords: External Transparency, External Pressures, Performance of Social Responsibility, Disclosure of Social Responsibility JEL Classification: D25, D53, M41 Introduction Although prior research has made significant progress in exploring the relationship between transparency and social responsibility, gaps remain in understanding how information asymmetry affects CSR performance. Existing literature presents mixed findings regarding transparency’s impact on CSR. Some studies suggest that transparency may reduce CSR investments due to short-term performance pressures (Aguinis & Glavas, 2012; Margolis & Walsh, 2003; Orlitzky et al., 2017), as noted by Fiesler (2011). Conversely, other research indicates that increased transparency may enhance CSR investments by attracting more analysts and bolstering corporate reputation (Luo et al., 2015; Gao et al., 2016). Studies also suggest that external pressures may incentivize firms to prioritize CSR activities to align with societal expectations (Garcia Sanchez et al., 2021). However, the literature remains inconclusive on whether transparency increases or decreases CSR investments. Prior research has predominantly examined transparency from an analyst’s perspective, whereas external stakeholder pressures compel managers to meet shareholder expectations and ensure financial performance (Pondville et al., 2013; Rowley & Berman, 2000). Anderson et al. (2009) categorize transparency into internal (disclosure quality) and external (market scrutiny), with the latter necessitating clearer information disclosure. External transparency, driven by external pressures, may influence CSR performance and disclosures—a relationship this study seeks to explore (Bushman & Smith, 2003). Methods & Materials The data for this study were collected from multiple sources, including the Tehran Stock Exchange database, the Tehran Stock Exchange Technology Management Company, and the Tehran Stock Exchange Library, which provided variables related to external transparency, bid-ask spread, and trading volume. Additional data for control variables were extracted from Rahavard Novin software, financial statements, and company notes, while the Board of Directors’ activity reports to the General Assembly of Shareholders supplied information on CSR and corporate governance quality. The sample comprises companies listed on the Tehran Stock Exchange from 2013 to 2022. Applying specific selection criteria, a sample of 105 firms was selected, yielding 1,050 firm-year observations. Preliminary data processing was conducted in Excel, while final analyses were performed using EViews (version 13) and Stata (version 17). Findings Regression model estimations indicate a positive and significant relationship between external transparency and both CSR performance and disclosure. The first hypothesis, examining the effect of external transparency on CSR performance, was confirmed, suggesting that increased transparency enhances CSR performance. The second hypothesis, tested via logistic regression, also confirmed a positive and significant association between external transparency and CSR disclosure, indicating that greater transparency leads to more robust CSR disclosures. Existing literature suggests that external stakeholder pressures for transparency help bridge the gap between disclosed and actual performance, preventing misleading CSR reporting (Anderson et al., 2009). Market expectations and oversight compel firms to present information more clearly (Leuz, 2000). Such monitoring pressures encourage firms to make more informed CSR decisions and better assess risks (Bushman et al., 2004). External transparency surpasses internal disclosure quality, which is often legally mandated, by incorporating external pressures that push managers toward higher disclosure standards (Bushman & Smith, 2003). As transparency pressures intensify, firms shift focus toward long-term performance, whereas reduced pressures may lead to short-termism rooted in agency theory. Discussion and Conclusion The findings align with prior research, demonstrating that external transparency positively influences CSR disclosure and performance. These results suggest that external pressures for transparency foster a more transparent informational environment, thereby improving CSR disclosures. Additionally, heightened transparency reduces information asymmetry and agency conflicts, aligning managerial and corporate objectives, which in turn enhances CSR performance. In Iran’s current economic climate—marked by sanctions and currency fluctuations—firms face elevated CSR-related risks. External transparency can serve as a critical tool in mitigating information asymmetry in financial markets, enabling firms to strengthen their market position through improved disclosure practices. شفافیت برون‌سازمانی فراتر از کیفیت افشای درون‌‌سازمانی است که براساس قوانین و مقررات انجام می‌‌گیرد. این جزء از شفافیت شامل الزامات و فشارهای بیرونی است که مدیران را ملزم به رعایت استانداردهای بالاتری در ارائۀ اطلاعات می‌کند؛ در این راستا هدف پژوهش حاضر بررسی تأثیر شفافیت برون‌سازمانی بر عملکرد و افشای مسئولیت اجتماعی است. این پژوهش ازنظر نوع، توصیفی-همبستگی بوده و جامعۀ آماری آن شامل ۱۰۵ شرکت پذیرفته‌شده در بورس اوراق بهادار تهران در سال‌های ۱۳۹۲ تا ۱۴۰۱ هجری شمسی است که با استفاده از نرم‌افزار ایویوز و استتا آزمون شده‌اند. نتایج نشان داد که افزایش شفافیت برون‌سازمانی منجر به بهبود عملکرد و افشای مسئولیت‌پذیری اجتماعی شرکت‌ها می‌شود؛ بنابراین، فشار ناشی از شفافیت برون‌سازمانی موجب افزایش شفافیت شرکت‌ها و بهبود افشای مسئولیت اجتماعی آنها می‌شود. افزایش این‎‌ نوع شفافیت، عدم تقارن اطلاعاتی و مشکلات نمایندگی را کاهش می‌دهد و اهداف مدیران را با اهداف شرکت همسو می‌کند که نتیجۀ آن بهبود عملکرد مسئولیت اجتماعی شرکت است. این پژوهش درک عوامل تعیین‌کننده گزارشگری مسئولیت‌پذیری اجتماعی را با نشان‌دادن اینکه شفافیت برون‌سازمانی به‌عنوان پیش‌بینی‌کننده‌ای قوی در زمینۀ فعالیت‌های مسئولیت‌پذیری اجتماعی است، افزایش خواهد داد؛ علاوه‌براین، با آشکارکردن تأثیر شفافیت برون‌سازمانی بر گزارش‌های مسئولیت‌پذیری اجتماعی مدیران را تشویق می‌کند تا گزارش‌های جامع‌تری تهیه کنند. این مطالعه سیاست و پیامدهای عملی را آشکار می‌سازد و نشان می‎‌دهد که چگونه فشارهای برون‌سازمانی برای شفافیت، شرکت‌ها را به سمت مسئولیت اجتماعی بیشتر سوق می‌دهد.

https://amf.ui.ac.ir/article_29407_df7786306f559aadf07aebe34995cdfc.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 13 4 2025 12 22 A Comparative Analysis of Option Pricing Models Under Jump Dynamics, Skewness, and Non-Normal Kurtosis مقایسۀ کارایی مدل‌های قیمت‎‌گذاری اختیار تحت پرش، چولگی و کشیدگی غیرنرمال 35 56 29408 10.22108/amf.2025.143713.1945 FA حسین نصرالهی کارشناسی ارشد، گروه پژوهشی مالی زاگرس، دانشکده علوم پایه، دانشگاه آیت الله بروجردی، بروجرد، ایران 0009-0006-6819-9513 محمدرضا حدادی دانشیار، گروه پژوهشی مالی زاگرس، دانشکده علوم پایه، دانشگاه آیت الله بروجردی، بروجرد، ایران 0009-0006-6819-9513 Journal Article 2024 12 18 The Black-Scholes model assumes log-normal stock returns with constant volatility, yet empirical evidence reveals significant deviations, including skewness and excess kurtosis in financial markets. To better capture these characteristics, extended models incorporating jump processes and non-normal distributions have been developed. This study evaluates the pricing accuracy of four option pricing models—the Black-Scholes model, the Merton jump-diffusion model, the Kou double-exponential jump model, and the Gram-Charlier expansion model—with a focus on their performance under varying degrees of skewness and kurtosis. Our findings indicate that the Gram-Charlier model outperforms the Merton and Kou models in scenarios with negative skewness and leptokurtic distributions. Conversely, the Kou model demonstrates superior accuracy under conditions of low skewness and kurtosis. These results highlight the importance of selecting appropriate pricing models based on the underlying return distribution characteristics. Keywords: Options, Merton's Diffusion Jump Model, Gram-Charlier Model, Skewness, Kurtosis JEL Classification: G11, G12 Introduction. The Black-Scholes model assumes that stock returns follow a normal distribution with constant volatility. However, empirical evidence in financial markets shows that stock returns exhibit significant non-normal skewness and kurtosis. To better capture these characteristics of asset return series, several models have been developed to generalize the Black-Scholes framework for more accurate option pricing. The Merton jump-diffusion model and the Kou model extend the Black-Scholes approach by incorporating a compound Poisson jump process, which allows these models to account for skewness and kurtosis in asset price distributions. An alternative methodology, the Gram-Charlier expansion, addresses skewness and kurtosis effects through a different approach - it uses Hermite polynomials to approximate the probability distribution of asset prices. This study systematically examines and compares the pricing accuracy of four key models: the standard Black-Scholes model, the Merton jump-diffusion model, the Kou model, and the Gram-Charlier expansion. Our analysis specifically focuses on how these models perform under varying conditions of skewness and excess kurtosis, providing insights into their relative strengths and limitations. Materials & Methods This study analyzes historical closing prices for three major Iranian financial instruments: (1) Iran Khodro Company (ticker: Khodro) from May 16, 2020 to August 28, 2024; (2) Social Security Investment Company (ticker: Shasta) from March 2, 2022 to August 28, 2024; and (3) Charisma Equity Fund (ticker: Aharm) from December 20, 2021 to August 28, 2024. Volatility estimates for all three securities were computed using historical volatility methods based on the price series. We then calculated European call option prices using four distinct pricing models: the standard Black-Scholes model, Gram-Charlier expansion, Merton jump-diffusion model, and Kou double-exponential jump model. Finally, we conducted a comparative analysis between the model-derived prices and actual market prices to evaluate model performance. Findings This study incorporates the effects of skewness and excess kurtosis into various option pricing models. Our comparative analysis reveals that the Gram-Charlier model demonstrates superior pricing accuracy, exhibiting lower errors compared to both the Merton jump-diffusion model and Kou model under conditions of negative skewness and leptokurtic distributions. Conversely, the Kou model outperforms alternative approaches in markets characterized by low skewness and kurtosis. These findings make two significant contributions to the option pricing literature. First, they provide empirical evidence for model selection criteria based on distributional characteristics of underlying assets. Second, they demonstrate that optimal model choice depends critically on specific market conditions. Our results suggest that practitioners should carefully consider the statistical properties of asset returns when selecting pricing models, rather than relying on a single universal approach. Discussion and Conclusion The results of this study indicate that for the Shasta symbol, option prices calculated using the Gram-Charlier model significantly outperform those derived from the Kou and Merton models. This superior performance stems from the presence of negative skewness and excessive kurtosis in the data. We conclude that the Gram-Charlier model produces better results than the Merton and Kou jump models when applied to datasets exhibiting negative skewness and high kurtosis. For the Khodro symbol, the Merton model demonstrates greater pricing accuracy compared to both the Gram-Charlier and Kou models, a result attributable to the high kurtosis present in this dataset. Regarding the Leverage symbol, the Kou model provides more accurate option pricing than the Gram-Charlier model, owing to the relatively normal skewness and kurtosis characteristics of the data. These findings lead to two key inferences: First, the Kou model proves particularly suitable for datasets displaying normal characteristics or for cases where normality has been rejected by statistical tests but without significant abnormal skewness and kurtosis. Second, the Merton model serves as an appropriate option pricing model for non-normal datasets characterized primarily by high kurtosis without accompanying skewness. مدل بلک شولز فرض می‌کند که بازدۀ سهام از توزیع نرمال با نوسان ثابت پیروی می‌کند. درصورتی‌که شواهد تجربی در بازارهای مالی نشان می‌دهند که بازدۀ سهام چولگی و کشیدگی غیرنرمال دارد. به‌منظور انعکاس بهتر ویژگی‌های سری بازدۀ دارایی‎‌ها، مدل‎‌هایی برای تعمیم مدل بلک شولز برای قیمت‌گذاری دقیق‌تر اختیار معامله معرفی ‌شده است. مدل مرتون، مدل کو و گرام‌چارلیه توسعه‌یافته‌هایی از مدل بلک-شولز هستند که با افزودن یک فرایند پرش مرکب پواسون، اثر چولگی و کشیدگی در چگالی قیمت دارایی‌ها را مدل‌سازی می‌کنند. این پژوهش دقت مدل‌های قیمت‌گذاری اختیار خرید معاملۀ بلک شولز، مرتون، مدل کو و گرام‌چارلیه را بررسی و مقایسه می‌کند و تأثیر چولگی و کشیدگی غیرنرمال را بر قیمت‌گذاری تحلیل می‌کند. نتایج نشان می‌دهد که مدل گرام‌چارلیه در شرایط چولگی منفی و کشیدگی غیرنرمال خطای کمتری از مدل‌های پرش انتشار مرتون و مدل کو دارد. درمقابل، در شرایط چولگی و کشیدگی پایین، مدل کو عملکرد بهتری از خود نشان می‌دهد.

https://amf.ui.ac.ir/article_29408_4726acf6619f001e1c80d3031ad868e3.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 13 4 2025 12 22 Behavioral Biases in Investor Decision-Making: A Comparative Meta-Analysis of Behavioral Finance Research تحلیل مقایسه‌ای سوگیری‌های رفتاری مؤثر بر تصمیمات سرمایه‌گذاران: شواهدی از فراتحلیل پژوهش‌های تجربی مالی رفتاری 57 76 29463 10.22108/amf.2025.144225.1956 FA سیدامیر ثابت کارشناسی ارشد، گروه مدیریت، دانشکده مهندسی صنایع و مدیریت، دانشگاه صنعتی شاهرود، شاهرود، ایران سعید آیباغی اصفهانی استادیار، گروه مدیریت، دانشکده مهندسی صنایع و مدیریت، دانشگاه صنعتی شاهرود، شاهرود، ایران عبدالمجید عبدالباقی عطاآبادی دانشیار، گروه مدیریت، دانشکده مهندسی صنایع و مدیریت، دانشگاه صنعتی شاهرود، شاهرود، ایران Journal Article 2025 02 01 Behavioral finance challenges traditional economic theories by demonstrating how cognitive and emotional biases systematically influence investor decisions. This study conducts a meta-analysis of 61 empirical studies (24 domestic, 37 international) to compare the effects of 12 prominent behavioral biases, selected based on their prevalence and diversity in the literature. Employing a random-effects model in CMA2 to account for heterogeneity, we quantify the biases' relative impacts using effect sizes, Z-scores, and hypothesis testing. Results reveal that mental accounting exhibits the strongest effect size, underscoring its dominant role in distorting financial decision-making. Overconfidence, loss aversion, and anchoring also demonstrate significant—though variable—influences. These findings consolidate fragmented behavioral finance research, offering empirical clarity on the comparative weight of key biases in investment behavior. Keywords: Investment, Behavioral Finance, Bias, Capital Market, Meta-Analysis JEL classification: E22، D03، O16 Introduction Prices in financial markets frequently diverge from fundamental values, even with rational participants, challenging traditional theories like the Efficient Market Hypothesis (EMH) and Modern Portfolio Theory (MPT). Classical finance assumes a structured decision-making process comprising problem recognition, solution identification, alternative evaluation, and optimal choice selection, but empirical evidence reveals systematic irrationalities, which behavioral finance addresses by incorporating psychological perspectives to demonstrate how cognitive and emotional biases distort investor behavior. This study synthesizes research on twelve key biases through meta-analysis, confirming their significant impact across markets, where cognitive biases include overconfidence (overestimating knowledge), anchoring (relying on initial reference points), herd behavior (following crowds), representativeness (using stereotypes over data), availability heuristic (overweighting recent information), and mental accounting (categorizing money subjectively), while emotional biases feature loss aversion (fearing losses more than valuing gains), regret aversion (avoiding potential regret), self-attribution (blaming failures on externals), optimism bias (overestimating success), and self-control issues (failing long-term planning). These biases stem from bounded rationality, time constraints, emotions, social pressures, and information asymmetry, with Prospect Theory further explaining irrationalities by showing how investors assess gains and losses asymmetrically, particularly when facing losses. By understanding these biases, markets can develop tools to mitigate their effects and foster more rational decision-making, as recognizing behavioral biases in investment decisions proves crucial for both investors and policymakers to help mitigate irrational choices and avoid unexpected financial risks, while this research aligns with global behavioral finance studies in emphasizing the need for bias-aware strategies to enhance decision-making stability. Methods This meta-analysis synthesizes empirical research on investor behavioral biases through a rigorous four-step methodology: (1) systematic literature review to identify relevant studies, (2) effect size calculation using standardized metrics, (3) heterogeneity testing via Q-statistics and I² to assess consistency, and (4) model selection (fixed- or random-effects) based on heterogeneity levels, with inclusion criteria requiring studies to examine at least one of 12 key biases (e.g., overconfidence, loss aversion), report statistical outcomes (effect sizes, p-values), and cover diverse markets including traditional assets and cryptocurrencies. Heterogeneity testing determined model selection, where studies demonstrating consistency (Q p ≥ 0.05) utilized a fixed-effects model while those showing significant variation (Q p < 0.05) employed a random-effects model, with effect size analysis testing two competing hypotheses: H₀ (bias X has no significant effect on decisions) and H₁ (bias X has a significant effect), where a Z-test p-value > 0.05 supported H₀ while p < 0.05 rejected it, thereby confirming a bias's influence. The study's key contributions include establishing a unified framework for assessing bias impacts across markets, maintaining methodological rigor through adaptive modeling approaches, and enabling cross-market comparisons that distinguish universal versus context-specific effects, while the findings consolidate fragmented behavioral finance research to offer investors and policymakers actionable insights for mitigating bias-driven risks, ultimately quantifying how cognitive and emotional biases shape investment behavior across different environments to provide robust, generalizable conclusions for improving financial decision-making. Conclusion and discussion This meta-analysis confirms that cognitive and emotional biases universally impact investor decision-making across global markets. The study used a random-effects model, justified by confirmed heterogeneity (I² and Q statistics with p<0.05). Results demonstrate all examined biases significantly influence financial behavior. Key findings reveal several important patterns. Overconfidence bias was universally validated. Anchoring bias showed mixed results, with one dissenting study. Herding and representativeness biases received unanimous support. While some studies contested loss aversion, availability, and regret aversion biases, the majority confirmed their significance. Notably, mental accounting emerged as the most influential bias. The research highlights how bias manifestation varies based on personal experiences and environmental factors. This suggests their prominence depends on context. Emotional biases appear particularly susceptible to recent events and temporal factors. Several limitations should be noted. There are insufficient quantitative studies on lesser-known biases and emerging markets. Future research should prioritize four key directions: First, longitudinal studies of bias evolution. Second, examining interaction effects between multiple biases. Third, investigating market-specific manifestations (traditional vs. crypto markets). Fourth, developing evidence-based mitigation strategies. The cryptocurrency boom presents a critical research gap, as most studies focus on traditional markets. Interdisciplinary collaboration with psychologists could yield practical interventions. Additionally, real-time behavioral tracking in digital markets may uncover new bias patterns. These advancements would help investors and policymakers counteract systematic decision-making errors. Such tools are especially valuable in today's increasingly complex financial ecosystems. The consistency of findings across domestic and international studies underscores how fundamental behavioral biases are to financial decision-making processes. مالی رفتاری از زمانی که پدید آمد توانست پاسخ‌گوی بسیاری از تناقضات اقتصاد کلاسیک در بررسی واقعیت‌های اقتصادی در جامعه باشد. مهم‌ترین نتایج به‌دست‌آمده از مالی رفتاری نشان از وجود سوگیری‌های رفتاری در برخورد با اتفاقات اقتصادی مختلف است. شناخت و بررسی تأثیر‌گذاری این تورش‌ها و مقایسۀ آنها با یکدیگر برای سرمایه‌گذاران و تصمیم‌گیرندگان برای جلوگیری از پیامدهای ناشی از این سوگیری‌ها بسیار مهم و اثرگذار است که مبنای این پژوهش، همین مقایسه بین این سوگیری‌هاست. در این پژوهش که بر مبنای فراتحلیل پژوهش‌های تجربی انجام‌گرفته در حوزۀ تورش‌های رفتاری است، 12 تورش اصلی شناسایی‌شده در کارهای دیگران باتوجه‌به منابع و کثرت پژوهش‌های صورت‌گرفته برای این تورش‌ها، انتخاب شد. این سوگیری‌ها براساس 61 مقاله مطالعه‌شده انتخاب شد، که 24 مقاله داخلی و 37 مقاله از منابع بین‌المللی بوده‌اند. داده‌های استخراج‌شده از مقاله‌ها، به نرم‌افزار فراتحلیل 2[1] وارد شد که طبق نتیجۀ خروجی از نرم‌افزار، ناهمگنی در بین نتایج همۀ پژوهش‌ها مشاهده شد و به همین دلیل از روش اثرات تصادفی برای تحلیل نتایج فراتحلیل استفاده شد. درنهایت تأثیر این تورش‌ها بر تصمیم‌گیری براساس آماره Z و سطح معناداری محاسبه‌شده توسط نرم افزار تأیید شد و بیشترین اندازه اثر برای سوگیری حسابداری ذهنی به دست آمد.

https://amf.ui.ac.ir/article_29463_0dc87b8bf98b1b35c277a493a7b24b02.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 13 4 2025 12 22 Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms پیش‌بینی احتمال وقوع ریسک عملیاتی در صنعت بانکداری با استفاده از الگوریتم‌های یادگیری ماشین 77 96 29425 10.22108/amf.2025.143030.1929 FA حامد نادری دانشجوی دکتری، مهندسی صنایع، گروه مهندسی صنایع، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران، ایران محمد علی رستگار سرخه استادیار، گروه مدیریت سیستم‌ و بهره‌وری، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران، ایران بختیار استادی دانشیار، گروه مدیریت سیستم‌ و بهره‌وری، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران، ایران مهرداد کارگری دانشیار، گروه مهندسی فناوری اطلاعات، دانشکده مهندسی صنایع و سیستم‌ها، دانشگاه تربیت مدرس، تهران، ایران Journal Article 2024 10 12 This study investigates and predicts the likelihood of operational risk occurrence in the banking industry using machine learning algorithms. The primary objective is to analyze operational risk data and evaluate the performance of various machine learning models to develop effective tools for enhancing risk management and minimizing financial losses in banks and financial institutions. Operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and k-Nearest Neighbors (KNN). Model performance was assessed using evaluation metrics such as accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC) to determine the most effective model for risk prediction. The findings indicate that the RF and SVM algorithms outperform other models in predicting operational risk across all scenarios. Furthermore, the results demonstrate the strong predictive capability of machine learning algorithms in assessing operational risk, highlighting their potential as valuable decision-making tools for risk management in the banking sector. Keywords: Risk Prediction, Operational Risk, Risk Management, Machine Learning Introduction Operational risk is defined as the risk arising from external factors or failures in internal controls or information systems, which may lead to both anticipated and unexpected losses (Crouchy et al., 1998). Lopez (2002) characterizes it as any unquantifiable risk that a bank may encounter. According to the Basel II Agreement, operational risk refers to the probability of loss resulting from deficiencies, breakdowns, or inefficiencies in human resources, processes, technologies, infrastructure, or internal and external events (Pena et al., 2018). To estimate the capital required to cover operational risk, the Basel framework introduces three approaches: the Basic Indicator Approach (BIA), the Standardized Approach (SA), and the Advanced Measurement Approach (AMA) (Mora Valencia, 2010; Mora Valencia et al., 2017). The BIA and SA estimate capital requirements based on annual gross income, with the key distinction being that the SA categorizes a bank’s activities into eight business lines. Under the BIA, an alpha coefficient (α) of 15% is applied, whereas in the SA, each business line has a specific beta coefficient (β) ranging between 12% and 18%. The AMA employs both quantitative and qualitative methods for operational risk modeling, leveraging databases to collect statistical data and utilizing the loss distribution approach (LDA) to model frequency and severity distributions. Capital coverage is then determined based on the cumulative distribution of these variables. Since the LDA is data-driven, the Basel framework (BCBS, 2004) emphasizes the necessity of a robust database for collecting operational risk data. Four key databases are required: internal loss event data, external loss event data, scenario-based analysis data, and a database of business environment and internal control factors. Compared to other banking risks, such as credit and market risks, measuring, monitoring, and managing operational risk is considerably more complex. This risk has gained increasing attention in recent years, as large operational losses have led to the liquidation of financial institutions (Abdymomunov et al., 2020; Afonso et al., 2019). Crisanto and Perino (2017) identify cyber threats and cyber fraud as critical factors influencing operational risk capital estimation. These risks have intensified with the growth of electronic banking services and include illegal access, system disruptions, and the misuse or theft of digital assets for financial gain (BCBS, 2016; Drew & Farrell, 2018). To quantify potential losses in electronic banking transactions, Bouveret (2018) proposed a Bayesian Network (BN) model to estimate operational risk capital requirements in financial institutions. Machine learning has emerged as one of the most promising yet challenging approaches in modern finance (Tsai & Wu, 2008). These methods have transformed the financial industry, with deep learning (DL) being extensively studied and applied due to its adaptability and predictive capabilities (Ivanov, 2019). Pena et al. (2021) employed a fuzzy convolutional deep learning model to estimate the maximum operational risk value at a 99.9% confidence level. Similarly, Zhou et al. (2020) utilized semi-supervised machine learning algorithms to classify operational risks based on financial news, analyzing 5,843 documents from financial articles and newspapers in the Asia-Pacific region between February and March 2019. Their model demonstrated the capability to predict various types of risks in the banking industry. In another study, Akbari and Yazdanian (2023) applied machine learning algorithms to determine optimal thresholds for operational loss severity data, classifying the data and estimating the capital required to cover operational risk by integrating severity and frequency distribution functions with Monte Carlo simulation. Method and Data In this study, operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including RF, DT, SVM, LR, NB, and KNN. The models' performance was assessed using evaluation metrics such as accuracy, precision, recall, F1-score, and AUC to identify the most effective model for predicting the likelihood of risk occurrence. Findings The results indicate that the RF and SVM algorithms exhibit strong performance in predicting operational risk across all scenarios. Specifically, the RF algorithm achieved an accuracy of 0.9690, while the SVM algorithm attained an accuracy of 0.9587 in State 1, making them the most effective models in this setting. Both algorithms demonstrated comparable performance across other modes. Conclusion and Discussion This study analyzes and predicts operational risk occurrence in the banking industry using machine learning algorithms. The findings indicate that various algorithms, particularly RF and SVM, demonstrate strong predictive performance. These results have the potential to transform operational risk management in banks, leading to significant reductions in associated costs and losses. A key insight from this study is that leveraging large and diverse datasets can substantially enhance prediction accuracy. Machine learning models can process complex datasets, identify hidden patterns, and facilitate early risk detection, enabling banks to implement preventive measures before risks materialize. Moreover, integrating machine learning into risk management enhances decision-making by providing precise, data-driven predictions, allowing for more effective strategies and efficient resource allocation. Future research could incorporate additional data, such as historical records, economic indicators, and internal process information, to further improve prediction accuracy. With advancements in technology, more sophisticated techniques—such as reinforcement learning methods (e.g., DQN, Q-Learning, DDPG, and Meta-Learning)—could enhance the accuracy and efficiency of operational risk prediction models. این پژوهش با هدف پیش‌بینی احتمال وقوع ریسک عملیاتی در صنعت بانکداری با استفاده از الگوریتم‌های یادگیری ماشین انجام شده است. پژوهش حاضر با تحلیل داده‌های ریسک عملیاتی و ارزیابی عملکرد الگوریتم‌های یادگیری ماشین به‌منظور ارائۀ الگوریتم‌هایی مؤثر برای پیش‌بینی دقیق‌تر احتمال وقوع ریسک عملیاتی و مدیریت بهتر آن در صنعت بانکداری صورت گرفته است. در این پژوهش، داده‌های مرتبط با ریسک عملیاتی از سال 1395 تا 1402 جمع‌آوری و پیش‌پردازش شد و سپس با استفاده از مدل‌های یادگیری ماشین مانند RF، DT، SVM، LR، NB و KNN پیش‌بینی انجام شد. عملکرد مدل‌ها با معیارهایی همچون دقت، صحت، بازخوانی، F1-score و AUC ارزیابی شد تا بهترین مدل برای پیش‌بینی احتمال وقوع ریسک انتخاب شود. نتایج نشان می‌دهند که الگوریتم‌های RF و SVM در پیش‌بینی احتمال وقوع ریسک عملیاتی در تمامی حالت‌ها عملکرد بسیار خوبی دارند؛ به علاوه که الگوریتم‌های یادگیری ماشین توانایی بالایی در پیش‌بینی وقوع ریسک عملیاتی دارند و می‌توانند ابزار مؤثری برای تصمیم‌گیری‌های مدیریتی در صنعت بانکداری فراهم کنند.

https://amf.ui.ac.ir/article_29425_a3f7f2ead626771faed263b0ed9ce778.pdf

دانشگاه اصفهان مدیریت دارایی و تامین مالی 2383-1189 13 4 2025 12 22 The Impact of Audit Quality on Organizational Capital and Financing Capacity تأثیرکیفیت حسابرسی بر سرمایۀ سازمانی و ظرفیت تأمین مالی 97 116 29487 10.22108/amf.2025.142982.1928 FA سمیه فتحی د‌انشجوی دکتری، گروه حسابداری، دانشکده مدیریت و اقتصاد، دانشگاه شهید باهنر کرمان، کرمان، ایران 0009-0006-1971-7812 مهدی بهار مقدم دانشیار، گروه حسابداری، دانشکده مدیریت و اقتصاد، دانشگاه شهید باهنر کرمان، کرمان، ایران 0000-0002-6449-4092 کاظم شمس الدینی دانشیار، گروه حسابداری، دانشکده مدیریت و اقتصاد، دانشگاه شهید باهنر کرمان، کرمان، ایران 0009-0006-1971-7812 Journal Article 2024 10 16 While audit quality remains one of the most widely examined topics in auditing research, its influence on corporate investment behavior and financing decisions remains underexplored. This study examines the dual impact of audit quality on organizational capital and financing capacity using data from 148 Tehran Stock Exchange (TSE) listed firms (2011-2022). Employing audit fees as an audit quality proxy within an Analytical Hierarchy Process (AHP) framework, we find audit quality significantly enhances organizational capital, suggesting high-quality audits facilitate strategic resource allocation and capital formation. Conversely, we document an inverse relationship between audit quality and financing capacity, revealing a dynamic interaction where rigorous auditing may initially constrain but ultimately strengthen firms' financial sustainability by improving credibility and access to stable funding sources. These findings contribute to the auditing literature by demonstrating audit quality's dual role as both an enabler of organizational development and a moderator of financial constraints. Keywords: Audit Quality, Organizational Capital, Audit Quality Metrics, Financing Capacity JEL Classification: M42, O16, G32 Introduction This study examines the dual role of audit quality in fostering organizational capital development and enhancing corporate financing capacity, addressing a significant gap in the literature regarding audit quality's influence on intangible investments. While prior research has established audit quality's importance in improving financial reporting transparency and reducing information asymmetry (DeFond & Zhang, 2014), its impact on strategic organizational assets remains underexplored. Organizational capital - the synergistic combination of knowledge, human capital, and physical assets - serves as a critical driver of value creation and competitive advantage (Georgantopoulos et al., 2022). We hypothesize that high-quality audits positively influence organizational capital by facilitating strategic investments (H1) and exhibit a complex, bidirectional relationship with financing capacity (H2), particularly valuable in economically volatile environments where access to external financing proves challenging (Lim et al., 2022). By analyzing Tehran Stock Exchange-listed firms, this study provides novel insights into how audit quality serves as an institutional mechanism that simultaneously nurtures intangible assets and improves financial access, thereby contributing to both corporate finance theory and practice. The findings offer meaningful implications for regulators and firms seeking to optimize their audit investments for both organizational development and financial sustainability. Metod and data This study employs panel data analysis of 1,776 firm-year observations (148 companies) from Tehran Stock Exchange-listed firms between 2011-2022, sourced from the Codal database and the Management Research, Development, and Islamic Studies Library. Following rigorous screening of the initial 5,496 observations from 458 companies, we implemented modified versions of Georgantopoulos et al.'s (2022) econometric models to examine audit quality's dual impact on organizational capital and financing capacity. Our methodological approach incorporates (1) multivariate regression analysis to assess the hypothesized relationships, (2) robustness checks to address potential endogeneity concerns, and (3) industry-adjusted measures to control for sector-specific variations. The selected models specifically account for firm-level characteristics while controlling for macroeconomic factors prevalent in emerging market contexts. Model (1): OCit=α+β1AQit+β2SIZEit+β3DEBTit+β4TOBINQit+β5FCFit+β6NCTit+β7GROWTHit+β8ISSUEit +β9SUBSit+β10ROAit+β11IOWNit+β12BINDit+β13YEARDUMit+β14INDDUMit +€it Model (2): FCit=α+β1AQit+β2SIZEit+β3DEBTit+β4TOBINQit+β5FCFit+β6NCTit+β7GROWTHit+β8ISSUEit+β9SUBSit+β10ROAit+β11IOWNit+β12BINDit+β13YEARDUMit+β14INDDUMit +€it Our measurement approach operationalizes organizational capital (OC) following Peters and Taylor's (2017) Model (3): ORGC it =(1-y0) ORGCit-1 +(SG&Ait * ð0) where y represents the depreciation rate, SG&Ait denotes total selling, general, and administrative expenses, and δ reflects the proportion of training costs to total SG&A expenses. Financing capacity (FC) is calculated as the annual interest expense-to-total debt payments ratio. To address audit quality measurement complexities, we employed the Analytical Hierarchy Process (AHP), administering a pairwise comparison matrix to 15 auditing scholars and practitioners. The consistency evaluation (consistency ratio < 0.1) identified audit fees (AQ_AF) as the optimal proxy, given its superior weighting score (0.72) across relevance, reliability, and data availability criteria. Findings Our empirical results support the first hypothesis, revealing a statistically significant positive relationship between audit quality and organizational capital (β = 0.04, p < 0.01), suggesting that each unit increase in audit fees corresponds to a 0.04-unit increase in organizational capital. This finding aligns with the theoretical expectation that high-quality audits contribute to knowledge accumulation and strategic decision-making enhancement through rigorous verification processes. Regarding the second hypothesis, we observe a significant negative association between audit quality and financing capacity (β = -0.12, p < 0.05), supporting the substitution effect hypothesis where firms facing financial constraints demand higher audit quality to compensate for increased information asymmetry. The results suggest an inverse dynamic equilibrium where improved financing capacity reduces the marginal benefit of audit quality, consistent with agency cost theory predictions. These findings collectively demonstrate audit quality's dual role as both an organizational capital enhancer and a financial constraint mitigator. Discussion and Conclusion This study reveals two key findings regarding audit quality's dual role: first, audit fees (as an audit quality proxy) exhibit a significant positive relationship with organizational capital (β=0.42, p<0.01), supporting the proposition that high-quality audits facilitate knowledge transfer and strategic investment in intangible assets, consistent with Georgantopoulos et al.'s (2022) findings. Second, we identify a significant negative association between audit quality and financing capacity (β=-0.31, p<0.05), confirming that firms facing financial constraints demand higher audit quality to mitigate information asymmetry and improve resource access, aligning with McNelly et al. (2019) and AlaviTabari and Hashemiyan (2011). These results collectively advance our understanding of audit quality's dual function as both an organizational capital enhancer and financial constraint moderator. The study contributes to the literature by empirically validating these relationships in an emerging market context, while suggesting future research avenues to examine audit quality's impact on other intangible assets like human capital within this framework. اگرچه کیفیت حسابرسی یکی از گسترده‌ترین حوزه‌های پژوهشی در حیطۀ حسابرسی محسوب می‌شود، شواهد محدودی دربارۀ نقش آن در فعالیت‌های سرمایه‌گذاری و تصمیم‌های تأمین مالی شرکت‌ها وجود دارد؛ براین‌اساس، هدف این پژوهش بررسی تأثیر کیفیت حسابرسی بر سرمایۀ سازمانی و ظرفیت تأمین مالی است. داده‌های مربوط به ۱۴۸ شرکت پذیرفته‌شده در بورس اوراق بهادار تهران طی دورۀ زمانی ۱۳۹۰ تا ۱۴۰۱ جمع‌آوری و تحلیل شد. در این پژوهش، از حق‌الزحمۀ حسابرسی به‌‌عنوان شاخصی برای سنجش کیفیت حسابرسی در چارچوب فرایند تحلیل سلسله‌مراتبی (AHP) استفاده شده است. یافته‌ها نشان می‌دهد که کیفیت حسابرسی تأثیر مثبت و معناداری بر سرمایۀ سازمانی دارد. این نتیجه بیانگر آن است که کیفیت حسابرسی بالا، با انتخاب‌های راهبردی در سرمایه‌گذاری‌های مرتبط با منابع سازمانی در ارتباط است و به‌عنوان عاملی کلیدی در شکل‌گیری سرمایۀ سازمانی عمل می‌کند. همچنین، کیفیت حسابرسی تأثیر منفی بر ظرفیت تأمین مالی شرکت‌ها دارد. این یافته نشان می‌دهد که رابطۀ بین کیفیت حسابرسی و ظرفیت تأمین مالی رابطه‌ای پویا است که ممکن است محدودیت‌های مالی شرکت‌ها را تعدیل کند و درنهایت منجر به افزایش اعتبار و دسترسی به منابع مالی پایدارتر شود.

https://amf.ui.ac.ir/article_29487_926ac894b4b3f6e7117c8e857b8c800d.pdf