Iranian Journal of Accounting, Auditing and Finance
Volume:10 Issue: 2, Spring 2026

Traditional financial models fail to capture the nonlinear, self-similar patterns in stock markets, whereas the Fractal Market Hypothesis (FMH) provides a more robust framework. This study aimed to carry out a comprehensive bibliometric analysis to map the intellectual structure and evolution of research on stock markets with fractal patterns. Based on 1,280 documents obtained from the Scopus database (1982–2025), bibliometric analyses were conducted including co-authorship and co-word analysis, as well as thematic evolution with the Bibliometrix R package. Despite limitations in study design, results indicated a significant shift from basic to their applied interdisciplinary research. Multifractal approach has emerged as the leading methodology with around 42% studies after 2005. Research output is led by China, India and the USA with increasing interest in financial crises, cryptocurrencies and hybridizing fractals with artificial intelligence. The bibliometric results yield empirical evidence that reinforces the applicability of the FMH in addressing market complexity and aligning with contemporary financial phenomena. Analysis of common themes in literature highlights certain knowledge deficiency, predominantly regarding comparative studies between developed and emerging markets, dynamic performance under systemic shocks of fractal structures and the utility function analysis based on where fractal-based trading strategies are implemented. This is clearly a stepping stone for more extensive research on that topic, but it also highlights how fractal approaches can span across academic fields in finance.

The main objective of this study was to examine the impact of the COVID-19 crisis on firms’ financial and market performance. The study employed quarterly data from a sample of firms listed on the Tehran Stock Exchange, covering 22 quarters 11 quarters before the COVID-19 crisis and 11 quarters during the pandemic. In total, 2,310 firm-quarter observations were analyzed using panel data methods in EViews 13 software. The findings indicated that during the quarters affected by COVID-19, the pandemic had a significant negative effect on the operating profit ratio and return on assets. In contrast, due to the inflationary conditions induced by the pandemic, COVID-19 exerted a significant positive effect on stock prices and, consequently, on firms’ market performance. Moreover, the results showed that adequate cash holdings mitigate the adverse effects of COVID-19 on both financial and market performance, suggesting that sufficient liquidity can serve as a financial buffer during crises. Conversely, a larger workforce amplifies the negative effect of COVID-19 on financial performance, given that COVID-19 is a human-centered crisis, while it does not influence the relationship between COVID-19 and market performance. Beyond confirming the expected negative impact of the COVID-19 crisis on firms’ financial performance, the findings highlighted two novel contributions. First, they showed that maintaining adequate cash holdings can serve as a defensive shield for firms during crises. Second, they revealed that a larger workforce may intensify the adverse effects of human-centered crises, such as the COVID-19 pandemic. Accordingly, the study suggested that firms should prioritize holding sufficient cash reserves as well as implementing human resource policies—such as training, preparedness, and succession planning—to better withstand future crises.

This study examined the peer effect on Environmental, Social, and Governance (ESG) disclosure in industries through imitative and reciprocal mechanisms and assessed the moderating roles of experience and company size. From 2012–2024, 117 different firms were listed on the Tehran Stock Exchange, making them part of the statistical sample. Principal Component Analysis (PCA) was used to generate the composite ESG index, and linear regression models were used to evaluate the hypotheses. A company's ESG disclosure was positively affected by its peers' ESG practices, according to the results, which showed a substantial intra-industry peer effect on ESG disclosure. The intra-industry peer impact was unexpectedly amplified by company experience. Large companies operating in the same industry showed mimetic peer influence, whereas small and large companies did not. On the other hand, whereas large and small enterprises were found to have a mimetic peer impact, small firms did not exhibit any reciprocal peer effect. This study took a fresh look at the impact of firms' size and experience as moderators of intra-industry peer effects on environmental, social, and governance (ESG) disclosure in Iran, differentiating between reciprocal and imitative dynamics. Policymakers and practitioners can use these findings to improve corporate transparency, and the literature on peer influence in ESG disclosure benefits from them as well.knowledge to enhance responsible accounting functions in the social context.

Auditor independence is essential to reliable financial reporting, yet prior research has largely focused on structural, regulatory, and economic determinants, with limited attention to the psychological factors that may influence auditors’ professional judgment. To address the gap, this study investigated how conspiracy illusion (the socio-psychological predisposition to see hidden motives and manipulations) influences auditor independence. Data from auditors in Iran, a newly emerging country molded by socio-cultural and religious factors that can impact on ethical decisions, is used to expand upon the moderating effects of tolerance for ambiguity and religious orientation. Additionally, examining psychological factors in large countries like Iran, where corporate governance is weak and a more permissive natural environment allows for greater individual expression, offers valuable insights. Using data from 300 auditors analyzed through structural equation modeling (SEM), the findings revealed a moderate but significant negative relationship between conspiracy illusion and auditor independence—higher levels of conspiracy illusion are associated with lower independence. However, auditors with greater tolerance for ambiguity and stronger religious orientation experienced a weakened negative effect of conspiracy illusion. These findings advanced understanding of auditor independence by demonstrating its shaping by professional and regulatory standards and underlying psychological and cultural factors. Consequently, fostering psychological resilience, critical thinking, and positive ethical and religious attitudes may help strengthen auditor independence, particularly in complex socio-cultural contexts like Iran.

The present study was conducted to compare the accuracies for ANN and XGBoost algorithms on predicting earnings management in listed companies of Tehran Stock Exchange. Earnings management is one way for managers to mislead their stakeholders, which can result in financial losses; therefore, accurate detection methods are important for earnings management and beyond statistical models. The present study used 2016–2021 quarterly financial data from 103 publicly traded companies in basic metals, automotive, chemical, food and pharmaceutical producing industries (5076 year−firm). Discretionary accruals were used and calculated by the Kasznik model to capture earnings management and split them into three groups: Increasing Accruals (+1), Decreasing Accruals (-1), and Near-Zero Accrual (0). A Confusion Matrix was used to perform the model evaluation. The results showed that the XGBoost algorithm is significantly superior to the ANN with an overall accuracy of 98.4%. With very few errors, all earnings management categories XGBoost had near to perfect results. In contrast, ANN demonstrated significant weaknesses leading to an overall accuracy of 63.1%. The study found that the model performance of XGBoost can further predict earning management with more accuracy thereby securing a process for financial institutions. Inspired by the relatively rare occurrence of applying XGBoost model, used for trilateral classification of increasing, decreasing and near-zero earnings management in emerging markets including Tehran Stock Exchange. This research contributes significantly to the literature by demonstrating the superior predictive power of ensemble learning methods over traditional neural networks in detecting financial misconduct, which offers a robust, high-accuracy tool for regulators and investors to enhance market transparency and reduce financial risk.

This study introduced a Deep Moderated Neural Network (DMNN) to model the non-linear interaction between emerging technology adoption and ethical leadership in shaping auditors’ professional judgment. Drawing on survey data from 151 auditors, were normalized five technology usage items and five ethical leadership items to [0,1] and constructed the target judgment score as the average of five professional-judgment items. We benchmarked the DMNN against four alternative methods ordinary least squares regression (OLS), OLS with explicit pairwise interactions (OLS+Int), support vector regression (SVR), and random forest (RF) using RMSE and R² on a held-out test set. The DMNN obtained an RMSE of 0.1014 and an R² of 0.7562 better than those by OLS (RMSE = 0.1035, R² = 0.703), OLS+Int (RMSE = 0.1333, R² = 0.508), SVR (RMSE = 0.1818, R² = 0.084) and RF (RMSE = 0.1006, R² =0.719). These results showed that the DMNN learns complex moderation effects much better and also achieved higher predictive accuracy and explained variance than linear- and traditional machine learning approaches. The DMNN demonstrated enhanced performance as well as theoretical interpretability to shed light on auditors’ judgment processes and represents a promising new analytical tool for auditing research moving forward.

Due to the societal scrutiny of banks' financial performance and concerns about conflicts of interest arising from the revolving door phenomenon among senior bank managers, this study addresses a critical gap in the literature by examining the impact of the revolving door on the performance of Iran's state-owned banks. Although most studies have concentrated on developed economies, few have examined this phenomenon in developing countries with centralized banking systems, such as Iran. Using data from nine state-owned banks over 20 years (2002–2021), the study incorporated a dummy variable from Athanasoglou et al. (2009) into the model to capture the movement of managers and board members between the Central Bank of the Islamic Republic of Iran and state-owned banks. The results revealed that none of the variables adjusted for the revolving door have a significant relationship with the dependent variable, return on assets. Thus, the revolving door phenomenon did not significantly affect bank performance in this context, suggesting that managerial transitions between the Central Bank and state-owned banks did not inherently lead to conflicts of interest. These findings called into question prevailing theoretical claims regarding the detrimental consequences of the revolving-door phenomenon and underscore the need to reassess governance frameworks within highly focused banking systems.

Stock market return prediction remains a highly challenging task due to the complex, dynamic, and noisy nature of financial markets. Although machine learning and deep learning models have been widely explored, many approaches struggle to capture long-term temporal dependencies and provide interpretable feature selection. In tackling these issues, the Temporal Fusion Transformer (TFT) is applied for multi-horizon time series forecasting of daily returns in Tehran Stock Exchange (TSE). Accordingly, the study proposes a new feature selection method called Initial Selected Features–Mutual Information Difference (ISF-MID), which enhances the traditional Minimum Redundancy Maximum Relevance (mRMR) algorithm by demonstrating a superior capability of handling redundancy and focusing on determining important parameters. Dual preprocessing works on ISF-MID, mRMR and PCA that improve the predictive performance, while also allowing for interpretable machine learning by working with a better representation of input. Mean Absolute Error (MAE), Mean Squared Error (MSE) and the coefficient of determination (R²) were used to conduct extensive comparisons with benchmark methods such as, Long Short-Term Memory LSTM, Multi-Layer Perceptron MLP, and Random Forest RF. Thus, the proposed method achieved competitive performance against benchmark architectures (i.e. TFT achieves R², of 98.9%, MAE 0.00043 and MSE 0.000018 on out-of-sample test, as well as high directional accuracy). Overall, the synergy of TFT with the ISF-MID feature selection strategy provides methodological novelty and practical significance, extending a potential framework for evidence-based return prediction and informed risk-central investment decisions.

This study examined the effect of working capital management (WCM) policies on cost stickiness, providing the first direct empirical quantification of whether and how financial constraints mediate this relationship in a major emerging market. Building on established trade-off, agency, and adjustment-cost theories, we hypothesize that aggressive WCM primarily reduces cost stickiness for efficiency, but that financial constraints partially offset this gain. Using 1,848 firm-year observations from 132 Tehran Stock Exchange (TSE)–listed firms (2010–2023), we estimated multiple regression and a covariance-based SEM. More aggressive WCM is associated with lower cost stickiness. Critically, the mediation analysis indicates partial mediation; aggressive WCM is linked to higher financial constraints, and these constraints, in turn, amplify cost stickiness, thereby quantifying the trade-off between direct efficiency gains and indirect financial rigidities. Our findings establish an empirical foundation for advising managers to adopt aggressive WCM strategies only when coupled with strategies to mitigate financial constraint risk. Policymakers, particularly in SME contexts, should design targeted liquidity provision mechanisms to unlock the full cost-flexibility potential of WCM.

As financial reporting becomes increasingly complex, both real earnings management (REM) and accrual-based earnings management (AEM) pose challenges for auditors in risk assessment and fee determination. While prior studies have examined these strategies in isolation, scant attention has been paid to their concurrent use, dual earnings management, particularly in audit pricing. Existing literature lacks analyses of the REM–AEM interaction within moderating frameworks, leaving unclear how AEM intensity moderates the REM–audit fee relationship. This gap impedes understanding of audit pricing in information-asymmetric settings and may lead to suboptimal audit judgments. To address this gap, this study investigated how AEM moderates the relationship between REM and audit fees. The study offered three novelties. First, it is the first in the domestic literature to examine the REM–AEM interaction. Second, it treated AEM as a moderator, revealing how auditors respond to compounded informational risk. Third, it provided evidence from the Iranian capital market, offering a practical framework for audit risk assessment in low-transparency environments. Using data from 72 firms listed on the Tehran Stock Exchange (2013–2025), results indicated a positive and significant relationship between REM and audit fees. A negative and significant interaction between REM and AEM emerges only when AEM is measured by the Kothari model. Results showed that using both strategies simultaneously affects auditors’ risk perceptions and weakens REM’s direct impact on audit fees. The study contributed to audit pricing theory by bridging behavioral and signaling perspectives, and presents practical implications for auditors as well as regulators and standard-setters