The Iranian Journal of Finance (IJF) is an international, open-access, double-blind, peer-reviewed, quarterly journal published by the Iran Finance Association (IFA), one of the leading scholarly organizations in the Middle East. All submitted manuscripts are screened for similarity using iThenticate software to ensure originality and authenticity, and are subsequently subjected to rigorous peer review by subject-matter experts. The IJF adheres to the guidelines of the Committee on Publication Ethics (COPE) and complies with the highest ethical standards in scholarly publishing.


  • Title: Iranian Journal of Finance
  • Publisher: Iran Finance Association
  • Plagiarism screening: iThenticate
  • Date of First Publication: Summer 2017
  • Review Process: Double blind peer review
  • Type of Articles: Original Article, Case-Study, Applied Article, Methodologies.
  • Type of Access: Open Access (OA)
  • Start Year open license: 2017 - Vol. 1, No. 1
  • Frequency: Quarterly
  • Document Type: Research Paper, Review
  • Time to submit to the reviewers: a maximum of a week
  • Publication fee: 19.500.000 Rials for publication
  • Language: English
  • Copyright: The Iranian Journal of Finance (IJF) is an open-access Journal licensed under the Creative Commons license (CC-BY 4.0) International License.

 

Call for Papers

1. The Iranian Journal of Finance (IJF) invites Researchers, Authors and Specialists to contribute to their Special Issue on AI and its Role in the Future of Financial Markets.

Guest Editor:

Farhad Reyazat, Ph.D., Fintech & AI Programs Director, London School of Banking & Finance, London, UK. (https://www.reyazat.com/my_bio) Email: reyazat@gmail.com

Guest Editor

Reza Raei, Ph.D., Professor, Department of Markets and Financial Institutions, Faculty of Accounting and Financial Sciences, College of Management, University of Tehran, Tehran, Iran. Email: raei@ut.ac.ir

Guest Editor:

Ali Namaki, Ph.D., Assistant Professor, Department of Markets and Financial Institutions, Faculty of Accounting and Financial Sciences, College of Management, University of Tehran, Tehran, Iran. Email: alinamaki@ut.ac.ir

   

There is a full Call for Papers at HERE, which includes a description of what is required, dates, and submission requirements.

 

Specific subtitles covered by the journal are: 

  1. Corporate Finance
  2. Investments 
  3. Islamic Finance
  4. Financial Markets and Institutions
  5. Financial Engineering and Risk Management
  6. Financial Econometrics and Quantitative Methods
  7. Banking and Insurance

         


 

Identification and Prioritization of Factors Affecting the Development of the Islamic Debt Securities Market (Sukuk) Using the Fuzzy Screening Technique

Pages 1-33

https://doi.org/10.30699/ijf.2025.536693.1530

Ali Namaki, Mohamad Tohidi, Hamidreza Yazdani, Saeid Abdali Gargari

Abstract This study examines the key drivers of the Islamic debt securities market (Sukuk). This Sharia-compliant financial instrument is gaining recognition for its role in promoting economic development through ethical, non-usurious financing. As it is being used more in sectors such as infrastructure, energy, housing, and development, Sukuk has emerged as a significant financing tool for both Islamic and international markets. Following a two-step research strategy, the study first identified and categorized key factors influencing Sukuk market development by conducting a qualitative analysis of academic literature, scientific reports, and institutional documents. Twenty-one main components were distilled at this stage. Experts' views were gathered and analyzed at the second stage, based on linguistic variables, a fuzzy ranking technique, and Excel-based modelling, to prioritize factors uncovered while managing uncertainty in expert judgment. The results indicate that increasing the liquidity of Islamic finance instruments is the most significant method for improving Sukuk market growth, followed by the Management of Issuance Costs of Islamic Financial Securities (Sukuk) and Risk management (including exchange rate, interest rate, and credit risk) within the Sukuk structure. The study offers policy relevance to policymakers, regulators, and stakeholders who aim to improve the Islamic finance ecosystem by promoting the development and efficiency of the Sukuk market through targeted, evidence-based policies.

Investigation of Residual and Conventional Momentum Strategies in Short-term and Long-term Time Periods (Evidence from Tehran Stock Exchange)

Pages 34-64

https://doi.org/10.30699/ijf.2025.467917.1480

Mohammad Hasan Nezhad, Mohammad Osoolian, Fatemeh Nadafi

Abstract Many researchers have attempted to explain the phenomenon of medium-term return continuation using modern financial theories. The excess return gained in the momentum investment strategy, in fact, compensates for unknown risks that current theories are unable to explain. Research indicates that various strategies can be beneficial at different maintenance periods. Various strategies generally involve a simple method in which they are formed based on the criterion of return over a certain period in the past and are maintained for a corresponding period in the future. Each investment strategy tends to generate excess returns based on the predictability of short-term price movements, as indicated by past performance. The purpose of this study is primarily to investigate the usefulness of residual momentum and conventional momentum strategies in the short-term and long-term. The time period of this study is from 2009 to 2018, and the general approach for calculations is based on the method described by Jegadeesh and Titman (1993), Blitz et al. (2011), and Blitz et al. (2020). The results of this study show no significant difference between residual and conventional momentum strategies in both short-term and long-term periods, indicating that both approaches exhibit similar risk-adjusted performance and forecasting capabilities.

Integrating Engineering Principles with Financial Asset Management: The Three-Sigma Approach in Financial Markets

Pages 65-90

https://doi.org/10.30699/ijf.2025.492342.1498

Seyed Jalal Tabatabaei

Abstract In an increasingly volatile and uncertain financial landscape, particularly within the cryptocurrency market, robust risk assessment methods are essential. This study introduces an interdisciplinary framework that applies engineering concepts, specifically the three-sigma (3σ) criterion, to financial asset management. Drawing on the analogy between structural stress and financial return volatility, this study conceptualizes market returns as a stochastic stress process and asset strength as a dynamically adjusted resilience threshold. Using LUNA coin as a case study, the research employs Monte Carlo simulations, statistical process control principles, and a range of statistical tests, including the Shapiro-Wilk, Kolmogorov–Smirnov, and ANOVA tests, to evaluate the probability of structural failure, modeled as the first passage beyond a critical return threshold. The results reveal a first breach probability of 1.96% and identify a failure threshold of –0.3838, highlighting the model's capacity to detect extreme downside risk more conservatively than traditional Value-at-Risk (VaR) approaches. These findings support the use of three-sigma thresholds in highly volatile markets and align with previous studies emphasizing tail-risk modeling and engineering-inspired risk measures. This framework not only improves the understanding of asset fragility in crypto markets but also provides a practical tool for dynamic and real-time risk management. This study contributes to the evolving field of financial engineering by bridging statistical design principles and asset resilience modeling, offering new insights for researchers, investors, and policymakers.

Smart-Beta Portfolio Optimization Using Machine Learning Techniques

Pages 91-116

https://doi.org/10.30699/ijf.2025.524928.1518

Fatemeh Salehirad, Farid Tondnevis

Abstract This study examines the integration of machine learning techniques with smart beta investment strategies to enhance portfolio performance. Traditional market indices often fail to meet investors' expectations, especially during volatile market periods, leading to a growing interest in alternative strategies such as smart beta methodologies. These strategies combine the cost and risk efficiency of passive investing with the performance advantages of active strategies by employing alternative weighting schemes based on financial factors such as value, quality, and momentum. In this research, Return on Invested Capital (ROIC) is selected as a value-based factor due to its strong reflection of a company's operational efficiency and value creation driver. We employ three machine learning models—Support Vector Regression (SVR), Random Forest, and XGBoost—to forecast ROIC based on various financial ratios. Each model is fine-tuned using Bayesian optimization techniques to achieve the highest forecasting accuracy. The dataset includes financial data from 85 manufacturing companies listed on the Tehran Stock Exchange. Model performance is evaluated using R², Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), with the optimized Random Forest model achieving the best results based on higher R² and lower error values compared to the other models. The forecasted ROIC values are then used to construct a smart beta portfolio, which is compared to a traditional market-cap-weighted portfolio. The findings demonstrate that a machine learning-enhanced, ROIC-based smart beta strategy can significantly outperform traditional approaches, offering investors a more robust and data-driven method for portfolio construction and risk-adjusted return enhancement.

Firm-Level Prediction of Money Laundering Risk in Iranian Listed Companies; an Integrated Quantitative-Qualitative Approach

Pages 117-139

https://doi.org/10.30699/ijf.2025.526296.1519

Alireza Saranj, Meysam Bolgorian, Mohammad Nadiri, Mojtaba Taghipour

Abstract The primary objective of this study is to develop a predictive model for money laundering risk in Iranian listed firms. Initially, firm-level money laundering risk is measured using auditor assessments of anti-money laundering (AML) activities disclosed in annual audit reports. Subsequently, a quantitative modeling approach is employed, using financial and governance-related variables identified in prior research. To validate the quantitative findings, a qualitative approach based on grounded theory is also applied to identify additional explanatory factors. This research follows a mixed-methods design, incorporating both quantitative and qualitative phases. In the quantitative phase, a panel logit regression model is estimated using data from 1,680 firm-year observations covering the period 2012–2023. Independent variables include firm size, return on equity, leverage, investment opportunities, board independence, and board size. In the qualitative phase, semi-structured interviews were conducted with 10 experts to identify key risk factors, followed by the design and administration of an 18-item questionnaire distributed to 110 professionals. Exploratory factor analysis was then used to extract latent variables. The quantitative analysis reveals significant relationships between money laundering risk and several variables, such as firm size (positive), return on equity (negative), leverage (positive), and board independence (negative). The qualitative analysis identifies three core factors: (1) organizational culture and employee training, (2) corporate governance, and (3) a composite factor comprising compliance, organizational complexity, financial performance, firm size, and capital structure. Together, these factors explain over 50% of the variance in expert responses. The convergence of results from both methodological approaches confirms the robustness of the proposed model. Corporate governance indicators—particularly board size and independence—alongside financial attributes such as firm size, profitability, and capital structure, are found to be significant predictors of firm-level money laundering risk. The findings underscore the importance of strengthening internal control mechanisms and compliance structures in reducing money laundering risk.

Risk prediction of investment funds in member countries of the Federation of European and Asian Stock Exchanges - Machine Learning Approaches

Pages 140-188

https://doi.org/10.30699/ijf.2025.522399.1527

Nashmil Esmaily, Parviz Piri, Ali Ashtab, Mehdi Heydari, Akbar Zavari Rezaei,

Abstract The main objective of this study is to compare the predictive accuracy of machine learning models, particularly Random Forest and Artificial Neural Networks, with classical statistical methods (such as Logistic Regression and Linear Discriminant Analysis) in forecasting the risk of Exchange-Traded Funds (ETFs) in member countries of the Federation of European and Asian Stock Exchanges. Furthermore, the study aims to identify the key performance and fundamental variables impacting the risk of these funds. This research adopts a quantitative approach based on secondary data analysis. Data were collected for the years 2015-2023 from the databases of the Federation of European and Asian Stock Exchanges and the Tehran Stock Exchange. After preprocessing, risk prediction models, including Random Forest, Artificial Neural Networks, Logistic Regression, and Linear Discriminant Analysis, were developed and validated for each country using unified evaluation metrics (such as accuracy and AUC). The statistical significance of differences in model performance was tested using non-parametric Mann-Whitney U tests, given the non-normal distribution of accuracy across countries. Sensitivity analysis was then conducted on the two superior machine learning models to determine the impact of independent variables (both performance indicators, such as Jensen's alpha and market return, and fundamental attributes, such as fund size and manager expertise) across different markets. Empirical results indicate that, across most countries and after harmonizing time and geographical dimensions, machine learning models, specifically Random Forest and Artificial Neural Networks, outperform classical statistical approaches in predicting ETF risk, with statistically significantly higher accuracy and AUC values (p<0.05 in Mann-Whitney U tests). The robustness of these findings is confirmed after controlling for heterogeneity among countries. Sensitivity analyses further reveal that both performance variables (e.g., Jensen's alpha, market return) and fundamental factors (e.g., fund size, manager expertise) have a significant impact on risk outcomes within these models. At the same time, machine learning methods exhibit a stronger ability to identify and quantify the importance of these variables compared to classical methods. The results highlight the practical advantage of adopting machine learning techniques for risk assessment and management in diverse international financial markets. Overall, the findings of this study reveal that employing machine learning models—especially Random Forest and Artificial Neural Networks—significantly improves the accuracy of ETF risk prediction and enables a more comprehensive identification of key risk factors compared to classical statistical approaches. These models demonstrate superior flexibility and the ability to capture complex, multidimensional data patterns, making them highly advantageous tools for financial risk management. The results suggest that integrating advanced machine learning techniques at both regional and international levels can enhance the responsiveness of investment systems to market changes, providing fund managers and investors with a more solid, data-driven basis for decision-making.

Pricing Embedded Options Using Fast Fourier Transform to Compare Variance Gamma and Black-Scholes-Merton Model Efficiency

Volume 9, Issue 2, 2025, Pages 54-69

https://doi.org/10.61186/ijf.2024.424421.1439

Alireza Barati, Maryam Khalili Araghi

Abstract Embedded options are virtually new instruments identical to options in many aspects except their non-tradable nature. Testing the efficiency of the Variance Gamma and Black-Scholes-Merton model on these instruments would provide a vision of transitioning from the classical model with its deficiency to more intricate models. Considering the complicated nature of the Variance Gamma stochastic process to price options, the Fast Fourier Transform (FFT) method is used in conjunction with the Nelder-Mead Simplex method to calibrate models. This research uses the Fast Fourier Transform (FFT) to price four embedded options with the ticker symbols Hefars912, Heghadir912, Heksho208, and Hetrol911 under the two models. The result approves that the Variance Gamma process is more efficient than the Black-Scholes-Merton model in pricing embedded options. Consequently, the variance gamma process would generate fewer errors in pricing those options that can be used in a practical sense.

Tehran Stock Exchange, Stocks Price Prediction, Using Wisdom of Crowd

Volume 7, Issue 4, 2023, Pages 1-28

https://doi.org/10.61186/ijf.2023.382999.1397

Babak Sohrabi, Saeed Rouhani, Hamid Reza Yazdani, Ahmad Khalili Jafarabad, Mahsima Kazemi Movahed

Abstract Two predominant methods for analyzing financial markets have been technical and fundamental analysis. However, the emergence of the Internet has altered the trading landscape. The availability of Internet and social media access plays a moderating role in information asymmetry, resulting in investors making informed decisions. Social media has turned into a source of information for investors. Through diverse communication channels on social media, investors articulate their perspectives on whether to buy or sell a stock. According to Surowiecki, the collective opinions gathered through social media frequently offer better predictions than individual opinions, a phenomenon referred to as the Wisdom of the Crowd. The wisdom of the crowd stands as an essential measure within social networks, with its potential to reduce errors and lessen information-gathering costs. In this study, we tried to evaluate the wisdom of the crowd's potential to improve stock price prediction accuracy. So, we developed a prediction model by Long Short-Term Memory based on the wisdom of the crowd. Users’ opinions in Persian about the Tehran Stock Exchange (TSE) stocks were collected from SAHMETO for eight months. The Support Vector Machine classified them into buy, sell, and neutral classes. During the research period, people mentioned 823 stocks, and 52 stocks with over 100 signals were chosen. The results of the study show that although the model presented has achieved an acceptable level of accuracy, correlations between the actual and predicted values exceeded 90%. The accuracy metrics of the proposed model compared to the base model were not improved.

A Profitable Portfolio Allocation Strategy Based on Money Net-Flow Adjusted Deep Reinforcement Learning

Volume 7, Issue 4, 2023, Pages 59-89

https://doi.org/10.61186/ijf.2023.364455.1369

Samira Khonsha, Mehdi Agha Sarram, Razieh Sheikhpour

Abstract Portfolio allocation with Deep Reinforcement Learning (DRL) has been the focus of many researchers. In investing, a portfolio optimization strategy is selecting assets that maximize return on investment while minimizing the risk. Asset optimization involves balancing risk and return, where stock returns are profits over time, and risk is the standard deviation value of the asset's return. Many of the existing methods for portfolio optimization are essentially the expansion of diversification methods for assets in the investment. Signiant drawdowns and early entry into the share are still challenging in portfolio construction. The idea is that having a portfolio based on net money flow is less risky than allocating a portfolio based on historical data only and turbulence as risk aversion. This paper proposes a profitable stock recommendation framework for portfolio construction using the DRL model based on the net money flow (MNF) indicator. We develop a new risk indicator based on the intelligent net-flow behavior of smart money to help determine the optimal market timing for buying and selling. The experimental results of real-world trading scenario validation show that the model outperforms all the considered baselines and even the conventional Buy-and-Hold strategy. Moreover, in this paper, the effect of defining different environments made of various information with hyper parameter optimization on the performance of models has been investigated, and the performance of DRL-driven models in different markets and asset positions has been investigated. The empirical results show the dominance of DRL models based on MNF indicators.

Portfolio Optimization with Systemic Risk Approach

Volume 9, Issue 1, Winter 2025, Pages 32-61

https://doi.org/10.61186/ijf.2024.446203.1461

Mohammad Azad, Mirfeiz Fallah Shams, Ali Rahmani, Teymour Mohammadi

Abstract Portfolio optimization has always been the main concern of investors. What differentiates different optimization models from each other is the risk measure. The main contribution of this paper is to provide a portfolio optimization model that considers systemic risk so that it can help investors make optimal investment decisions as a general model. For this purpose, two models are presented. In the first model, systemic and systematic risk were considered simultaneously, and in the second model, only systemic risk was considered. In the two mentioned models, delta conditional value at risk (∆CoVaR) and the Markowitz model are used respectively to measure systemic risk and a benchmark model. Also, the criteria used to compare the performance of the reviewed models include the ratio of reward-to-risk, along with the Sortino ratio and the Omega ratio. The problem of optimization and examination of the results was carried out on a selected sample, 38 companies listed in the Tehran Stock Exchange (TSE) from 2013 to 2023. The results of empirical analysis of out-of-sample data (during a period of 1198 days) show that based on all three mentioned criteria, the first proposed model shows the best performance among the three models. In addition, the performance of the second model is ranked second. In short, it can be said that considering systemic risk in portfolio optimization leads to better performance than the Markowitz model.

Comparative Analysis of Missing Values Imputation Methods: A Case Study in Financial Series (S&P500 and Bitcoin Value Data Sets)

Volume 8, Issue 1, 2024, Pages 47-70

https://doi.org/10.61186/ijf.2024.414027.1427

Mahdi Goldani

Abstract The accurate imputation of missing values in time series data is paramount for maintaining the integrity and reliability of analyses and predictions. This article investigates the effica-cy of various missing values imputation methods, encom-passing well-known machine learning and statistical tech-niques. Moreover, for a better understanding, they imple-mented two financial data time series: S&P 500 and Bitcoin markets spanning from 2016 to 2023 on a daily frequency. Initially utilizing complete datasets, controlled missingness was introduced by randomly removing 45 data points. Then, these methods applied multiple imputation strategies for estimating and substituting these missing values. Experi-mental evaluation yielded insightful findings regarding the performance of the different methods. The examined ma-chine learning methods, including k-Nearest Neighbors (k-NN), Random Forest, Deep Learning, and Decision Trees, consistently outperformed their statistical counterparts, such as Mean Imputation, Regression Imputation, Hot-Deck Im-putation, and Expectation-Maximization Imputation. Nota-bly, Random Forest emerged as the most effective method, showcasing superior performance in terms of accuracy and robustness. Conversely, the Mean Imputation method exhibited com-paratively inferior outcomes, suggesting its limited suitabil-ity for financial time series data. This research contributes to the ongoing discourse on data integrity within finance ana-lytics and serves as a comprehensive guide for practitioners seeking optimal missing values imputation methods. The empirical evidence provided herein advances the under-standing of imputation techniques' relative performance and their application in financial data, facilitating enhanced de-cision-making processes and yielding more reliable predic-tions.

Number of Volumes 9
Number of Issues 34
Number of Articles 203
Number of Contributors 504
Article View 151,800
PDF Download 129,338
View Per Article 747.78
PDF Download Per Article 637.13
 
Number of Submissions 572
Rejected Submissions 286
Reject Rate 50
Accepted Submissions 173
Acceptance Rate 30
Time to Accept (Days) 221
Number of Indexing Databases 24
Number of Reviewers 210
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