Accounting, Finance and Computational Intelligence

The aim of this study is to identify the components of corporate stock returns using novel composite variables in the Tehran Stock Exchange. Employing a systematic review and meta-synthesis approach, the researcher analyzed the findings and outcomes of previous scholars. Through the application of the seven-step method proposed by Sandelowski and Barroso, the influential factors were identified. Out of 553 articles, 51 were selected based on the CASP method, and the validity of the analysis was confirmed with a Kappa coefficient of 0.747. In this context, the Kappa index was used to assess reliability and quality control, and the value indicated an excellent level of agreement for the identified indicators. The analysis of the collected data using MAXQDA software led to the identification of 48 initial concepts based on 12 indicators across 4 dimensions. To identify the components of stock returns using novel composite variables in the Tehran Stock Exchange, the meta-synthesis technique was applied. The identified dimensions include financial and economic factors, behavioral and emotional factors, technological and data-driven factors, and institutional and regulatory factors. The findings of this study indicate that stock returns are influenced by a network of diverse factors that interact in complex ways. Attention to these factors and the adoption of appropriate strategies in investment management, economic policymaking, and the development of technological and regulatory infrastructures can enhance market efficiency and increase investor returns. Therefore, innovative and comprehensive analytical approaches are deemed essential for a better understanding of the mechanisms influencing the stock market.

The present study aimed to investigate the indicators of the FinTech business model in alignment with corporate social responsibility (CSR) practices and the improvement of corporate financial performance. The participants of this research included university professors and managers. Individuals were selected using a purposive sampling method. The sample consisted of 20 experts and specialists. The data collection instrument comprised two components: (1) the examination and analysis of upstream documents and financial planning documents in the library section, and (2) semi-structured interviews in the field section. The semi-structured interviews with participants continued until the point of theoretical saturation. For the analysis of qualitative data, the thematic analysis method based on the Attride-Stirling model was employed. To ensure validity, the interview questions were reviewed and approved by three financial planning and management experts—one holding a master’s degree and two holding PhDs. To assess reliability, Krippendorff’s alpha coefficient was used, which was confirmed. The ATLAS.ti software was utilized for the thematic analysis. The results from the factor analysis indicated that among the 192 existing indicators (items), 48 first-order constructive themes were identified, leading to the derivation of 12 second-order constructive theme categories. The second-order constructive themes of the research model included: service innovation, operational financial transparency, organizational social accountability, environmental sustainability, customer-centric value creation, digital data security, sustainable profitability growth, equity in access, intelligent risk management, stakeholder commitment, organizational decision-making agility, and alignment with social mission. Ultimately, four quantitative criteria were used to evaluate credibility, transferability, confirmability, and dependability. The results were as follows: the level of expert agreement, measured using Holsti’s coefficient (PAO) or “Percentage Agreement Observed,” was found to be 0.810, which is a significant value. Given the critiques of the Holsti method, the P-Scott index was also calculated, yielding a value of 0.813. The fourth index used to estimate qualitative research credibility was Cohen’s Kappa coefficient, which reached 0.804 in this study. Finally, Krippendorff’s alpha was also used, and it was estimated at 0.852 in this study.

Investments in productive assets intended for the modernization and expansion of production processes—and in response to market demand—are influenced by a range of internal and external organizational parameters, which together shape the investment strategy model. Considering the critical role that investment plays in profitability and the attainment of financial competitive advantage within businesses, this study aims to identify the factors influencing investment and to develop a model for investment strategy. The firms within the study’s scope are divided into two categories: chemical companies and food & beverage companies, with the results of the model implementation analyzed and compared across these two groups. In order to ensure compatibility of the model with environmental fluctuations and internal organizational changes, the variables of the business cycle and the corporate life cycle are incorporated into the model. The research methodology follows a mixed-methods approach comprising both qualitative and quantitative phases. The study's findings, covering the period from 2016 to 2023, reveal that the declining trend in the value of fixed assets indicates equipment obsolescence and a critical need for investment—particularly in the chemical sector, which relies heavily on imported advanced technologies. However, under current sanctions, such investment is unfeasible. Moreover, due to the presence of ownership entities and existing conflicts of interest in this profitable industry, excess investment is observed, which—based on the findings—is ineffective and lacks the necessary efficiency. In the food industry group, regulated pricing policies have led to reduced profitability and financial constraints, thereby preventing the sector from making the necessary levels of investment despite its strategic importance.

This study examines the role of futures contracts in financial risk management within the supply chain under the volatile economic conditions of Iran. In light of challenges arising from price and currency fluctuations, financial instruments such as futures contracts are analyzed as a strategy to mitigate risk and enhance stability across the supply chain. The research introduces a mathematical model based on a hybrid of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), integrating them into a Hybrid Genetic-Particle algorithm (HGP), to account for critical factors such as risk aversion levels, demand variability, and price volatility. The dataset utilized in the study was extracted from the Iran Khodro Company. The results demonstrate that the HGP algorithm outperforms both GA and PSO in terms of accuracy for risk prediction and factor loading, exhibiting a lower margin of error in modeling long-term risks and price fluctuations. Moreover, the sensitivity analysis of futures contracts reveals that these instruments can be effectively employed for risk hedging under conditions of economic instability. This study offers a practical framework and proposes strategies to improve supply chain management within domestic industries, emphasizing the need to strengthen financial infrastructure and establish transparent regulatory frameworks. Based on the findings, it is recommended that the HGP model be applied in key industries such as automotive manufacturing and petrochemicals to evaluate its operational efficiency. Additionally, the development of artificial intelligence software for risk monitoring, enhancement of decision-making processes, and the formulation of standardized methodologies for risk assessment and contract valuation by regulatory bodies can significantly contribute to the reinforcement of financial infrastructure.

The misery index is a key indicator for assessing a society's economic condition and its impact on public welfare. An increase in this index signals an intensification of economic problems that affect the well-being of individuals. Consequently, the misery index functions as an important tool for policymakers to adopt appropriate decisions aimed at improving economic conditions. Particularly during periods of crisis and economic recession, this index serves as a warning signal, highlighting the urgent need for corrective policy measures. One of the variables influencing the misery index is the exchange rate. Given the significance of the misery index and recent exchange rate surges in recent years, the present study evaluates the causal relationship between the exchange rate and its volatility with the misery index over the period from Spring 2001 to Summer 2024. Considering the numerous economic fluctuations in the country and the likelihood of structural breaks, the study employs the Fourier approximation to account for structural breaks in the stationarity tests of the variables, cointegration, and causality tests. In addition, the GARCH method is used to estimate exchange rate uncertainty for the purpose of examining the causal relationship between exchange rate uncertainty and the misery index. The results indicate a unidirectional causal relationship from the exchange rate to the misery index and a bidirectional causal relationship between exchange rate uncertainty and the misery index. Therefore, exchange rate instability can have damaging effects on the economic welfare of the population (misery index). Since external shocks primarily influence the national economy—and subsequently inflation and unemployment—through the exchange rate and its fluctuations, it is essential to prioritize the monitoring and forecasting of such shocks and the use of tools that can enhance the resilience of the domestic economy against them.

The capital market is one of the most critical sectors of the economy, and its status is closely linked to the overall economic structure of a country. Due to the transparency and speed of transactions, the stock exchange is recognized as a significant investment option. In other words, an active stock market facilitates corporate financing and channels idle and often unproductive small-scale capital into productive ventures. In most countries, stock indices are considered one of the most reliable primary indicators for evaluating financial markets. The Tehran Stock Exchange's overall index reflects the general trend of Iran’s capital market and indicates the broader status of the investment landscape. Therefore, understanding and analyzing the overall index and its fluctuations can assist capital market participants in making more informed decisions. Additionally, the stock index is widely used by financial researchers to compare the performance of the capital market with other markets and to measure the impact of various factors on market returns. This study is applied in terms of its objective and descriptive-analytical in terms of its nature. It falls within the category of ex-post facto research (using data from March 20, 2012, to March 19, 2024). The estimation method employed in the study is the Structural Vector Autoregression (SVAR) approach. The present article examines behavioral modeling of stock index volatility with a focus on market risk and trading volume fluctuations. Initially, the research model is specified, followed by stationarity testing using the Phillips-Perron method. Next, cointegration tests are conducted using the Johansen-Juselius approach. Subsequently, the model is estimated, and stability tests, impulse response functions (IRF), and variance decomposition analyses are carried out. The results of model estimation indicate that the coefficients of most key variables influencing the Tehran Stock Exchange’s overall index volatility align with the theoretical foundations of the subject. The main variables that are essential and interpretable within the results of the SVAR model include impulses originating from market risk premium, global gold ounce price fluctuations, exchange rate fluctuations, Bitcoin price volatility, OPEC oil price volatility, and fluctuations in stock trading volume. Based on the estimation results, Bitcoin price and market risk premium have no significant impact on stock index volatility. However, the other explanatory variables in the model significantly affect stock index fluctuations at a 95% confidence level.

It can be confidently stated that sudden crashes in financial markets over the past hundred years have been among the most significant events in human societies—events that, in addition to inflicting substantial losses on a vast group of investors, have led to major decisions. The occurrence of recent global financial crises and the subsequent abrupt collapse in stock prices of companies in financial markets, which caused significant losses for numerous investors, has attracted the attention of many financial researchers and scholars toward the topic of financial market crashes and their prediction. The sharp decline in financial market prices causes substantial losses to investors’ wealth and undermines their trust in capital markets. This study investigates the estimation of the financial market crash rate model in Iran, with an emphasis on the dynamic behaviors of the free float stock index and dividend yield, using time series econometrics. The study period spans from 1996 to 2023 in Iran. The model estimation was conducted using the Autoregressive Distributed Lag (ARDL) technique. This study is applied in its objective and descriptive-analytical in nature, and it falls under the category of ex post facto research. According to the results of the model estimation, the coefficient of the free float stock index is −0.173730, with a corresponding p-value of 0.0046, indicating a statistically significant impact at the 95% confidence level on the financial market crash rate in Iran during the study period. The coefficient of the dividend yield index in the model is −0.213467, with a corresponding p-value of 0.0061, demonstrating that the dividend yield index also has a statistically significant impact on the financial market crash rate in Iran within a 5% error margin. The coefficients of the variables for the Top 50 Companies Index, the Financial Index, and the Industry Index (with one lag) are −0.949789, −0.755780, and −0.514797, respectively. The associated p-values are 0.0018, 0.190, and 0.0212, respectively, indicating that these variables also exert a negative and significant impact on the financial market crash rate in Iran at a 95% confidence level.

The objective of this study is to explore the impact of financial technology on the emergence of money laundering, with an emphasis on the moderating role of financial requirements and financial literacy. Given the significant growth of fintech tools and platforms, it is essential to analyze their effects on financial risks, particularly in the realm of economic crimes. While the emergence of financial technology—through innovative solutions such as digital payments, cryptocurrencies, and decentralized platforms—has brought about positive transformations in financial access, it has also, due to features such as anonymity, high speed, and the elimination of intermediaries, become an attractive tool for financial criminals and introduced new risks into the money laundering process. The aim of the present research is to examine the moderating effects of financial requirements and financial literacy on the relationship between financial technology and money laundering. This study is categorized as applied research in terms of its objective and employs a survey-based methodology for data collection. The statistical population comprises financial managers and employees of institutions affiliated with the Iranian Association of Certified Accountants in Tehran. Based on Morgan’s table, the sample size was determined to be 384 individuals, selected through a random sampling method. The validity and reliability of the questionnaire were confirmed, and the data were analyzed using PLS3 software. The findings indicate that financial technology has a direct effect on the phenomenon of money laundering, and this impact can be mitigated when effective financial requirements and high levels of financial literacy are present. The results suggest that the presence of clear financial regulations and users’ financial awareness plays a significant role in preventing the misuse of innovative financial technologies for money laundering purposes. Moreover, the interaction among the study’s variables demonstrates that regulatory frameworks and financial education are crucial in strengthening financial transparency. Overall, the integration of financial technology with efficient regulatory institutions and financial education may lead to a reduction in risks associated with financial crimes.