Business, Marketing, and Finance Open

Presenting a Predictive Model of Stock and Over-the-Counter Market Price Indices Based on Macroeconomic Indicators Using Artificial Neural Networks

Ahmad Goodarzi

; Afshin Nokhbeh Fallah *

; Farid Sefaty

, Alireza Ghiyasvand

The primary objective of this study is to present a predictive model for stock and over-the-counter (OTC) market price indices based on the examination and prediction of the effects of macroeconomic variables on Iran’s capital market. To improve prediction accuracy, the study employed deep neural network algorithms to forecast future trends in stock price indices. In this research, the required data were collected from the Central Bank of Iran, the Statistical Center of Iran, the Gold and Currency Information Website, and the Securities and Exchange Organization (SEO) database for stock price index data. The analysis was conducted using two artificial neural network approaches: the Long Short-Term Memory (LSTM) network and the Multilayer Perceptron (MLP) trained through the backpropagation algorithm. The models were evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and prediction accuracy percentage. The implementation was carried out in the Python programming environment over the period from 2014 to 2023. The findings of the study indicated that the LSTM neural network model did not yield satisfactory prediction results. However, the MLP model successfully demonstrated the predictive impact of macroeconomic variables on the stock and OTC market price indices. Overall, the results suggest that the application of artificial neural networks, as an advanced analytical tool, provides an effective and efficient approach for analyzing and predicting stock market fluctuations. This study emphasizes the importance of considering macroeconomic variables in the analysis and forecasting of stock market volatility. Furthermore, the findings can serve as a valuable guide for economic policymakers and investors in making optimal decisions within financial markets.

Econometric Analysis of the Effects of Dividend Policy Changes on the Implied Cost of Equity Capital under Different Levels of Return on Assets

Sepideh Aqaei

; Meysam Arabzadeh *

; Yasser Rezaei Pitenoei

, Hassan Ghodrati

, Mohammadtaghi Kabiri

This study conducts an econometric analysis of the effects of changes in dividend policy on the implied cost of equity capital, considering the moderating role of return on assets (ROA). The main research question is whether the relationship between changes in dividend payouts and the cost of capital is influenced by the level of firm profitability. The primary objective is to test the hypothesis that the negative relationship between dividend increases and the cost of capital is stronger for firms with higher ROA. Methodologically, this is an applied research employing panel data from 120 companies listed on the Tehran Stock Exchange over an eight-year period (2016–2023). The implied cost of equity capital was calculated using the Gebhardt et al. model, and the relationships among the variables were examined using fixed-effects regression and interaction modeling. The findings revealed that, in the baseline model, an increase in dividend payouts leads to a decrease in the cost of capital. However, the key finding of the study indicated that this relationship alone is not statistically significant and is entirely moderated by firm profitability. The negative and significant coefficient of the interaction term between dividend changes and ROA confirmed that the mitigating effect of dividend increases on the cost of capital is considerably stronger and more reliable in firms with higher asset returns. This result suggests that firm profitability acts as a boundary condition determining how the market interprets dividend signals and, consequently, how the cost of capital responds.

The Role of Earnings Quality and Future Returns: A Rational Decision-Making Simulation Approach

Morteza Hadadi

; Alireza Ghiyasvand *

; Farid Sefaty

This study investigates the simultaneous effects of earnings quality, dividend policy, tax management, and firm growth—together with investor behavioral biases—on future stock returns and the mechanisms of rational decision-making in the Tehran Stock Exchange. Data were collected from 129 companies over a 22-year period (2001–2023) and analyzed using dynamic panel econometric methods. To simulate the decision-making process and assess the priority of variables, an artificial neural network and a payoff matrix based on game theory were employed. The findings indicate that high earnings quality, stable dividend policy, and conservative tax management increase future returns. Conversely, an aggressive tax approach and low-quality accruals lead to a decrease in returns. Incorporating behavioral indicators into the model significantly enhances its explanatory power, and the interaction between fundamental variables and behavioral biases plays a decisive role in the intensity of their effects. The game-theoretic portfolio model also confirms that the combination of high accounting compliance and proper tax management yields the highest probability of buy recommendations. Accurate prediction of stock returns requires an integrated approach in which both fundamental and behavioral information are simultaneously evaluated. Within such a framework, financial variables serve as the foundation for decision-making, but their final weighting is adjusted according to the psychological state of the market. The contribution of this study lies in providing an analytical tool for investors and policymakers to optimize portfolios, reduce the cost of capital, and enhance market efficiency.

Identification and Analysis of Barriers to the Realization of Synergy Between the Supreme Audit Court of Iran and Internal Auditing in the National Iranian South Oil Company

Aref Bouzar

; Ali Mahmoudi *

; Ali Hossein Hosseinzadeh

, Parisa Zolfaghari

This study aims to identify and analyze the barriers hindering the realization of synergy between the Supreme Audit Court of Iran and internal auditing within the National Iranian South Oil Company. The research employs a qualitative approach based on grounded theory. Data were collected through semi-structured interviews with experts in auditing and financial supervision and analyzed using open, axial, and selective coding methods. The findings indicate that barriers to achieving synergy fall into seven main categories: structural and organizational barriers, legal and regulatory barriers, cultural and behavioral barriers, human resource constraints, technological and information system barriers, political and managerial challenges, and finally, financial resource limitations. The results reveal that the presence of these barriers reduces the effectiveness of collaboration and synergy between the Supreme Audit Court and internal auditing, thereby affecting the efficiency of the supervisory system in large state-owned enterprises. The precise identification of these barriers can provide a foundation for designing corrective strategies and enhancing institutional synergy to promote transparency, accountability, and financial integrity in public sector organizations.

Designing a Model and Ranking the Digital Transformation Components Affecting the Quality of Accounting Information in Iran’s Capital Market: A Fuzzy Delphi Approach

Abbas Baharipour

; Shiva Hassanpour *

; Jaber Mohammad Moosaee

, Hossein Jannat Makan

The purpose of this study is to identify, rank, and present a model of digital transformation components that influence the quality of accounting information in Iran’s capital market. In recent years, the expansion of technologies such as artificial intelligence, cloud computing, and blockchain has transformed financial and accounting processes, emphasizing the need to reexamine the factors that affect the quality of financial information. This research is applied in terms of purpose and employs a mixed (qualitative–quantitative) method. In the qualitative–quantitative phase, the Fuzzy Delphi technique was used to collect expert opinions and to fuzzify/defuzzify judgments for ranking the components. The statistical population consisted of 18 academic and professional experts who reached a consensus after three Delphi rounds. The findings indicate that 12 main components can be classified into three dimensions: technological (such as artificial intelligence, cloud computing, blockchain, and accounting automation), control–security (such as digital internal controls and data governance), and human–institutional (such as digital literacy and technology acceptance culture). The ranking results showed that artificial intelligence, automation, and cloud computing have the highest importance in improving the accuracy, transparency, and reliability of information. Practically, the findings can serve as a foundation for developing a digital transformation roadmap for financial institutions, listed companies, and regulatory bodies, leading to enhanced financial reporting quality and increased investor confidence.

A Spatial–Deep Learning Hybrid Model for Cryptocurrency Pricing and Optimal Trading Strategy Design for Capital Management

Alireza Zamanian

; Mohammad Aslani *

; Mahmud Hematfar

The purpose of this study is to develop a hybrid model for accurately forecasting the returns of the 32 leading cryptocurrencies in the market and assessing systemic risk. The model is designed to overcome the limitations of linear and single models in capturing nonlinear spatial dependencies and the complex temporal dynamics of cryptocurrency markets. The research was conducted using daily data over the period from 2018 to 2023. A two-stage approach was applied: first, nonlinear spatial dependencies and market regime structures were analyzed using spatial econometric models; second, a hybrid framework combining spatial model predictions with several advanced deep learning models—including Transformer, Graph Neural Network (GNN), and Attention-based Neural Network—was developed to achieve the highest forecasting accuracy. The results indicated that spatial contagion among cryptocurrencies is a nonlinear phenomenon whose intensity peaks during crisis regimes. Moreover, Bitcoin and Ethereum account for over sixty percent of systemic risk. In the forecasting phase, the Transformer model achieved the best single-model performance; however, the hybrid model demonstrated absolute superiority across all performance metrics, particularly in financial and risk management measures (e.g., the Sharpe ratio), showing significant improvement over the best standalone model. Accordingly, the findings confirm that the spatial–deep learning hybrid model provides a comprehensive, robust, and highly accurate framework for cryptocurrency market prediction. The model underscores that success in this market requires the simultaneous consideration of structural effects, spatial dependencies, and nonlinear temporal patterns (deep models). This framework serves as an effective tool for systemic risk management and for designing trading strategies with risk-adjusted returns.

Providing an Appropriate Model for the Commercialization of Entrepreneurial Ideas in Knowledge-Based Companies with an Export-Oriented Approach Using a Mixed-Methods Research Design

Majid Torabi

; Mostafa Hashemi Tilehnouei *

; Younos Vakil Alroaia

In today’s global economy, companies that emphasize marketing and innovation are more capable of achieving a sustainable competitive advantage. On the other hand, the core driver of growth and development in the export sector is increasingly knowledge-based. The success of knowledge-based companies depends on their sales performance and market share; the more effective they are in the commercialization of goods and services, the greater their access to national and international markets will be. Accordingly, the present study aimed to propose a model for the commercialization of entrepreneurial ideas in knowledge-based companies with an export-oriented perspective, employing a mixed-methods research design (qualitative–quantitative). In the qualitative phase, the grounded theory approach was applied. Data were collected through semi-structured interviews with 21 experts, including university faculty members, specialists, and practitioners in the service sector, using purposive judgmental and snowball sampling. Data analysis followed the three-step coding procedure of open, axial, and selective coding. The resulting paradigmatic model consists of six causal categories (social factors; individual factors; organizational–technological factors; financial–economic factors; and legal–regulatory factors), an intervening category (mindset and support of transformational managers and leaders, universities and educational organizations, and the government), a contextual category (individual contextual conditions, socio-economic deficiencies, and intra-organizational factors), strategies (customer and market orientation, innovation orientation, international entrepreneurship culture, creativity and innovation training, and structural and industrial transformation), and consequences (individual outcomes, short-term and long-term results, national results, and regional results). In the quantitative phase, structural equation modeling (SEM) using the Partial Least Squares (PLS) technique was applied to test the proposed model. The findings revealed significant and positive reciprocal effects between internal and external latent variables. Considering the R² and Q² indices, the model demonstrated strong predictive power, and the overall model fit was high with a goodness-of-fit index of 0.659.

Designing a Comprehensive Stress Index for the Tehran Stock Exchange and Its Causal Relationship with the Gold Coin and Foreign Exchange Markets

Elham Babaheidarian

; Farhad Hanifi *

; Mir Feiz Fallahshams

This study aims to design a Comprehensive Stress Index (SSI) for the Tehran Stock Exchange using multivariate GARCH models (DCC-MGARCH) and machine learning (Random Forest) and to examine its causal relationship with the gold coin and foreign exchange markets. Daily time series data of selected Tehran Stock Exchange indices from November 22, 2014, to November 21, 2024, were collected and analyzed. First, the systemic risk (∆CoVaR) of each index was calculated; then, the optimal weights were determined using the Random Forest model, and the SSI was constructed following the methodology of Holo and colleagues (2012). Stability, shock, and predictability tests confirmed the validity of the index. The Granger causality test revealed a significant one-way causal relationship from the foreign exchange market to the SSI (p-value = 0.028), while no significant relationship was observed with the gold coin market. These findings highlight the influence of exchange rate fluctuations on the systemic risk of the stock market and provide a useful tool for policymakers.

About the Journal

Business, Marketing, and Finance Open (BMF OPEN) is an esteemed open-access journal dedicated to fostering high-quality academic research in the fields of business, marketing, and finance. Our mission is to create an inclusive platform for scholars, practitioners, and professionals globally to disseminate their research findings. BMF OPEN stands as a bridge between academic theory and practical application, ensuring that insights are accessible not only to scholars but also to industry leaders and policymakers.

Owner and Publisher: MAHERPUB (Address: No. 40, South Jamalzadeh Street, Azadi Street)

BMF OPEN s committed to promoting knowledge that can positively impact the business landscape, especially in light of the rapid evolution of digital marketing, financial technology, and sustainable business practices. By offering open-access content, we eliminate barriers to accessing important research and encourage widespread engagement with our published materials.

Our journal operates under a double-blind anonymous peer-review process to ensure the highest standards of academic integrity. Each manuscript is reviewed by two or three experts to provide constructive feedback that helps refine and enhance the quality of the work before publication.

BMF OPEN welcomes a variety of research articles, including but not limited to original empirical studies, conceptual papers, systematic reviews, and case studies. We are dedicated to publishing work that is innovative, rigorous, and of significant practical relevance to both academia and industry.

Current Issue

Vol. 2 No. 6 (2025): Serial Number 12

Published: 2025-11-01

Articles

Presenting a Model of Deficiencies and Governance Problems in the Banking System

Masoud Mohammadi

; Hossein Kazemi *

; Farzin Rezaei

1-13
- PDF
https://doi.org/10.61838/bmfopen.201
Presenting a Model of Digital Transformation Affecting the Quality of Accounting Information in Iran's Capital Market

Abbas Baharipour

; Shiva Hassanpour *

; Jaber Mohammad Moosaee

, Hossein Jannat Makan

1-14
- PDF
https://doi.org/10.61838/bmfopen.186
Applying the Neural Network Method in Analyzing the Preferences of Food Store Customers

Karim Layegh Ahani

, Younos Vakil Alroaia

; Abolfazl Danaie *

; Farshad Faezi Razi

1-23
- PDF
https://doi.org/10.61838/bmfopen.253
The Impact of Earnings Quality on the Probability of Bankruptcy in Companies Listed on the Iraq Stock Exchange: An Instrumental Variables Approach

Dunya Majeed Mozan Al-Saedi

, Masood Fooladi *

, Mezban Mohammed Farhan

, Saeid Sharifi

, Maryam Farhadi

1-10
- PDF
https://doi.org/10.61838/bmfopen.230
Development of a Paradigmatic Model of Earnings Management in the Automotive and Parts Manufacturing Industry

Samaneh Elahi Roodposhti

; Ebrahim Chirani *

; Seyyed Mozaffar Mir Bargkar

1-12
- PDF
https://doi.org/10.61838/bmfopen.245
Identifying Causal, Contextual, and Intervening Factors Influencing the Likelihood of Financial Fraud in Companies

Seyyed Saeid Mousakazemi

; Faegh Ahmadi *

; Mohammad Hossein Ranjbar

1-9
- PDF
https://doi.org/10.61838/bmfopen.244
Providing an Appropriate Model for Identifying and Evaluating the Factors Affecting the Quality of Internal Controls

Mehdi Asharion Ghomizadeh

; Mohammad Hamed Khan Mohammadi *

; Mohammad Mahmoodi

1-15
- PDF
https://doi.org/10.61838/bmfopen.214
Application of Artificial Intelligence Algorithms in Volatility Trading of the Global Tether Market: Trend Analysis, Forecasting Modeling, Proposed Framework, and Performance Evaluation

Mohammad Minaei

; Masoumeh Fazeli *

1-17
- PDF
https://doi.org/10.61838/bmfopen.255
Identifying and Prioritizing Strategies for Reviving Legacy Iranian Brands Using the Fuzzy Delphi and TOPSIS Approaches

Diar Karim Majeed

; Saman Sheikhesmaeili *

; Heirsh Soltan Panah

1-12
- PDF
https://doi.org/10.61838/bmfopen.249
Specialized Accelerator for Pharmaceutical Exports: A Local Business Model Proposal

Mahmoud Zamani *

1-11
- PDF
https://doi.org/10.61838/bmfopen.267
Proposing a Liquidity Risk Management Model in Iraqi Banks

Ahamad Jabbar Mahammad

; Mahdi Madanchi Zaj *

; Gholamreza Zomorodian

, Hamidreza Kordlouie

1-12
- PDF
https://doi.org/10.61838/bmfopen.263
The Causal Relationship Between Inflation and Inflation Uncertainty in Iran Under Structural Breaks

Mohammad Rezvani

; Mahdi Pendar *

; Elham Vafaei

, Milad Atghaei

1-11
- PDF
https://doi.org/10.61838/bmfopen.270
Presenting a Model for Fraud Prevention in the Public Sector Using Forensic Accounting

Ebrahim Nasiri

; Khadijeh Eslami *

; Razieh Naderkhani

, Khadijeh Rabiee

1-14
- PDF
https://doi.org/10.61838/bmfopen.261
A Model for Predicting Cryptocurrency Prices Using Meta-Synthesis Methods

MohammadDaniyal Jahed

; Zadollah Fathi *

; Gholamreza Zomorrodian

1-12
- PDF
https://doi.org/10.61838/bmfopen.271
Designing a Localized Model of Personal Branding in the Food Industry

Mercedeh Mortazavi Rad

; Mahmoud Ahmadisharif *

; Alireza Rousta

1-12
- PDF
https://doi.org/10.61838/bmfopen.275
Analysis of the Effect of Geopolitical Risks on Economic Resilience in Iran

Roa'a Mohammed kadhim

; Sara Ghobadi *

; Hussein Shnawa Majeed Al-Zerkani

, Hossein Sharifi Renani

1-10
- PDF
https://doi.org/10.61838/bmfopen.279

View All Issues

Business, Marketing, and Finance Open

Presenting a Predictive Model of Stock and Over-the-Counter Market Price Indices Based on Macroeconomic Indicators Using Artificial Neural Networks

Econometric Analysis of the Effects of Dividend Policy Changes on the Implied Cost of Equity Capital under Different Levels of Return on Assets

The Role of Earnings Quality and Future Returns: A Rational Decision-Making Simulation Approach

Identification and Analysis of Barriers to the Realization of Synergy Between the Supreme Audit Court of Iran and Internal Auditing in the National Iranian South Oil Company

Designing a Model and Ranking the Digital Transformation Components Affecting the Quality of Accounting Information in Iran’s Capital Market: A Fuzzy Delphi Approach

A Spatial–Deep Learning Hybrid Model for Cryptocurrency Pricing and Optimal Trading Strategy Design for Capital Management

Providing an Appropriate Model for the Commercialization of Entrepreneurial Ideas in Knowledge-Based Companies with an Export-Oriented Approach Using a Mixed-Methods Research Design

Designing a Comprehensive Stress Index for the Tehran Stock Exchange and Its Causal Relationship with the Gold Coin and Foreign Exchange Markets

About the Journal

Current Issue

Articles

Presenting a Model of Deficiencies and Governance Problems in the Banking System

Presenting a Model of Digital Transformation Affecting the Quality of Accounting Information in Iran's Capital Market

Applying the Neural Network Method in Analyzing the Preferences of Food Store Customers

The Impact of Earnings Quality on the Probability of Bankruptcy in Companies Listed on the Iraq Stock Exchange: An Instrumental Variables Approach

Development of a Paradigmatic Model of Earnings Management in the Automotive and Parts Manufacturing Industry

Identifying Causal, Contextual, and Intervening Factors Influencing the Likelihood of Financial Fraud in Companies

Providing an Appropriate Model for Identifying and Evaluating the Factors Affecting the Quality of Internal Controls

Application of Artificial Intelligence Algorithms in Volatility Trading of the Global Tether Market: Trend Analysis, Forecasting Modeling, Proposed Framework, and Performance Evaluation

Identifying and Prioritizing Strategies for Reviving Legacy Iranian Brands Using the Fuzzy Delphi and TOPSIS Approaches

Specialized Accelerator for Pharmaceutical Exports: A Local Business Model Proposal

Proposing a Liquidity Risk Management Model in Iraqi Banks

The Causal Relationship Between Inflation and Inflation Uncertainty in Iran Under Structural Breaks

Presenting a Model for Fraud Prevention in the Public Sector Using Forensic Accounting

A Model for Predicting Cryptocurrency Prices Using Meta-Synthesis Methods

Designing a Localized Model of Personal Branding in the Food Industry

Analysis of the Effect of Geopolitical Risks on Economic Resilience in Iran

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