Financial and Banking Strategic Studies

Abstract Purpose: In recent years, the emergence of innovative financial instruments in capital markets has not only increased the attractiveness of these markets for investors but has also led to advancements in financial knowledge, particularly in the field of behavioral finance. Given the importance of familiarity with this domain alongside technical and fundamental analysis, this research investigates the relationship between stock liquidity and excess financial leverage, considering the moderating role of agency costs in companies listed on the Tehran Stock Exchange. Methodology: This study is descriptive-correlational in nature. The required data were collected using the systematic elimination method for the period from 2017 to 2023. Findings: The findings indicate a significant relationship between stock liquidity and excess financial leverage. However, the moderating role of agency costs in this relationship was not confirmed. Originality/Value: The results of this research can be highly valuable for policymakers, managers, and investors in improving financial decision-making and optimizing capital structure management. By examining the moderating role of agency costs, this study contributes to a better understanding of the relationship between stock liquidity and excess financial leverage.

Abstract Purpose: Portfolio management and risk management are the most important issues in the investment world. In this regard, identifying and analyzing the mutual relationships and correlations between assets plays a key role in reducing volatility and risk management. This study investigates the relationships among the volatility of the stock market assets of Omid Investment Management Company, which is the largest asset of Sepah Bank. Methodology: To examine network connections, the daily returns of selected stocks in the portfolio of Omid Investment Management Company, chosen as the statistical sample, were examined, along with the relationship between volatility. The method we used in this study to examine volatility was the Vector Autoregression (VAR) model, which examined system-level connections by producing a variance decomposition table. We study the connections from April 2019 to September 2023. Findings: We found out changes in the returns of the GOLG1, GZIZ1, and CHML1symbols have the greatest impact on the returns of the portfolio companies of Omid investment management group, as the largest stock market asset of Sepah Bank, and GOLG1, SPAH1, and CHML1 symbols are most affected by changes in the returns of the portfolio companies of Omid investment management group. Originality/Value: By studying the network topology of variance decompositions, this research presents a new method for examining asset connections and will help managers optimize their portfolios by identifying riskier assets.

Abstract Purpose: Traditional cost accounting classifies costs into two types: fixed and variable. The implicit assumption is that the relationship between cost and activity is symmetric for increases and decreases in activity. In contrast, asymmetric cost behavior constitutes a new way of thinking about cost behavior. The cost stickiness model is considered an alternative to cost behavior, which is caused by the driving forces of cost behavior, resource adjustment, and commitment decisions made by managers. Therefore, based on this argument, the current research aims to examine the relationship between cost stickiness and the conclusion of bank contracts.
Methodology: The current research method is quantitative, correlational, and causal, and the hypothesis analysis method is correlational. The statistical sample of this research comprises 24 private and public banks listed on the Tehran Stock Exchange, selected via simple random sampling during the period 2014 to 2023.
Findings: The results of the hypothesis showed that cost stickiness increases the likelihood of bank contracts. It means that when activity levels decrease, managers of banks with sticky costs respond more slowly to cost reductions, leading to lower cost savings. Therefore, as bank sales or expected cash flows decline, the risk of bank default increases.
Originality/Value: Cost stickiness is expected to be associated with a higher cost of debt for several reasons. First, more sticky costs may lead to greater profit variability. Banks with more sticky costs show a greater decline in profits than banks with less sticky costs.

Abstract Purpose: This study aims to optimize investment portfolios by combining Data Envelopment Analysis (DEA) and machine learning, using multi-source data from the Tehran Stock Exchange. The main goal is to provide an advanced model for stock selection and portfolio optimization, enabling investors to adopt strategies more efficient than traditional methods.
Methodology: First, the DEA model was used to evaluate stock efficiency based on historical returns and asset correlations. Then, using a Support Vector Machine (SVM) and combining multiple sources of data, the trend in stock price movements is predicted. To improve the model's accuracy, random search and network methods were used to optimize the algorithm's hyperparameters. Finally, the resulting data are integrated into a portfolio optimization model, and a proposed investment strategy is formulated.
Findings: Experimental results on the Tehran Stock Exchange data showed that the proposed model can improve investment strategy performance compared to traditional methods. Sharpe and Sortino ratios indicate that the proposed model outperforms the minimum global variance strategy. It was also found that a low-diversity investment strategy can be more efficient than a fully diversified one.
Originality/Value: This study proposes a new approach to stock selection and portfolio optimization by combining DEA and machine learning. The use of multi-source data and advanced machine learning methods improves the accuracy of investor forecasting and decision-making, paving the way for future research in areas such as fuzzy models, meta-heuristic algorithms, and the analysis of relationships among financial indicators.

Abstract Purpose: This study examines the impact of predictor variables, including Information and Communications Technology (ICT) infrastructure, the level of interoperability in banking systems, human resources, financial resources, and policies and regulations, on the development of big data infrastructure within the Iranian banking system. Methodology: Data were collected using a Likert-scale questionnaire administered to 104 employees from Meli, Saderat, Mellat, Tejarat, and Sepah banks in the Hamedan province. Findings: Correlation tests revealed that all predictor variables are positively and significantly related to big data infrastructure. Multiple regression analysis further indicated that these factors explain approximately 56% of the variation in big data infrastructure (R² = 0.560). Additionally, the instrument's reliability was confirmed, with Cronbach's alpha values ranging from 0.769 to 0.821. Originality/Value: The findings are consistent with previous domestic and international studies, underscoring the importance of improving ICT infrastructures, enhancing system interoperability, strengthening human resource expertise, securing appropriate financial investments, and formulating supportive policies as fundamental prerequisites for the development of big data technology. Consequently, addressing these factors may pave the way for increased efficiency and competitiveness of the Iranian banking system in the digital era.

Abstract Purpose: This study examines the impact of salience theory on the cross-sectional return predictability of cryptocurrencies and evaluates it as a risk factor in asset pricing. The study seeks to analyze the role of investor behavioral biases in shaping return fluctuations in the cryptocurrency market. Methodology: Using data from over 4,000 cryptocurrencies with a market capitalization above one million USD during the period from January 2014 to June 2025, a Salience theory was constructed based on the difference between salient weighted returns and average value-weighted returns. The empirical analysis was conducted through portfolio sorting, Fama-MacBeth regressions, and the Liu-Tsyvinski-Wu three-factor model. Findings: The results show that cryptocurrencies with salient positive returns tend to underperform in subsequent periods, whereas those with salient negative returns tend to yield higher future returns. The effect of the ST index is statistically and economically significant and remains robust after controlling for other behavioral and fundamental factors. The index also explains well-known anomalies such as skewness preference, prospect theory, and downside beta. Originality/Value: This study is the first to introduce the ST index as a novel and effective behavioral factor in the cryptocurrency market. The findings demonstrate its strong predictive power and its ability to explain cross-sectional pricing patterns, outperforming traditional models and other behavioral factors.