Iranian Journal of Finance

The concept of liquidity creation has received much attention in project financing, as increased liquidity facilitates easier access to financial resources for long-term projects (Berger & Bouwman, 2009). However, the liquidity creation process is often accompanied by risk. Despite its advantages, if not managed properly, it can cause problems for the banking system and even the entire economy. On the other hand, capital is considered an influential variable in risk management, which helps the bank control challenging conditions. In this regard, the present research was conducted to investigate the moderating role of capital in the relationship between liquidity creation and failure risk, and further tried to examine the role of the monetary policy adopted by the central bank, considering the macro effects of this variable. This applied research project examined the banks admitted to the Tehran Stock Exchange from 2012 to 2018. The results showed that by controlling the variables of interbank interest rate and the variety of loans and deposits, liquidity creation is significantly and directly associated with failure risk. Moreover, the findings confirmed the moderating role of bank capital in the relationship between liquidity creation and failure risk. However, the monetary policy adopted by the central bank revealed an insignificant effect on this relationship. Therefore, decision-makers should consider these factors in the decision process.

Predicting stock price fluctuations has always been one of the most important financial challenges due to the complexities of financial data and nonlinear market behavior. This research aimed to analyze and compare the performance of machine learning algorithms in predicting the closing price jump of Iran Khodro Company shares. Two different methods of managing unbalanced data, NearMiss and SMOTE, were used to overcome the challenge of unbalanced data. The results showed that the NearMiss method outperformed SMOTE by balancing precision and recall in machine learning models. The CatBoost model was recognized as the best machine learning model in this study due to its stable performance in NearMiss and SMOTE methods. The CatBoost model showed a perfect balance between evaluation indicators in the NearMiss method, with an accuracy of 91.46% and an F1 score of 91.29%. This model also had high precision (93.18%) and acceptable recall (89.52%), which showed the ability to detect jumps and avoid wrong predictions correctly. On the other hand, in the SMOTE method, the Random Forest model was superior, with an accuracy of 85.08%. These results show that a combination of unbalanced data management methods and advanced machine learning algorithms can significantly improve the accuracy of price volatility prediction. The results of this research can help investors and financial analysts make better decisions in risk management and optimizing investment strategies.

The main goal of managing for-profit companies is to maximize shareholders' wealth, and to achieve it, management needs to make decisions regarding the sources and uses of capital. Different theories have been tried to explain the relationship between capital structure and performance, and have estimated a different relationship for financial leverage and company performance in different conditions. Financial leverage and operational leverage are two factors that influence the performance and macro policies of the company in terms of the profitability process. This research examines the effect of operational leverage and financial leverage on the company's profitability and financial distress, and finally examines the effect of replacing these two Leverages to maximize the profitability process and reduce the risk of financial distress. The sample includes 263 companies listed in the Tehran Stock Exchange and the Iran OTC Company from 2011 to 2021, which have passed the four screening factors of this study. The fixed effect panel regression method was used to test the first and second hypotheses. The results indicate a positive relationship between operational leverage and the profitability of companies, while the relationship between financial leverage and the profitability of companies is negative. On the other hand, both Leverages increase the risk of financial helplessness. To improve profitability, operational leverage can be replaced by financial leverage. Increasing operational leverage and reducing financial leverage can be used as a tool to grow the company's profitability; however, to reduce the helplessness of the companies and improve the profitability process, operational leverage can be replaced with financial leverage. Flexibility in financing companies is more than flexibility in the operational sector; that is why replacing operational leverage with financial leverage is more appropriate to improve profitability and reduce the risk of financial helplessness.

This study aims to demonstrate the performance of algorithmic trading strategies compared to traditional trading methods in artificial financial markets. This research uses a hybrid model based on agent-based modeling and machine learning methods to simulate agents' behavior in an artificial financial market. This model includes two categories, traditional agents and intelligent agents. Traditional agents are divided into three groups: liquidity providers, liquidity consumers, and noise traders. Intelligent agents are trained using deep learning techniques and recurrent neural networks. Based on the developed algorithms, the agent-based model simulates both categories of traditional and trained agents in an artificial financial market. Sensitivity analysis tests were used to test the validity and reliability of the model, and the values of the fat-tailed distribution of returns, volatility clustering, autocorrelation of returns, long memory in order flow, concave price impact, and extreme price events are calculated in the model and compared with the standardized values. Historical data was used to predict stock prices, and model simulations were used to generate trading signals and update the limited order book. The results of executing the model show the ability of intelligent agents to trade in artificial financial markets compared to traditional agents.

This research aims to model the accounting of startup companies within the financial business ecosystem.This research is philosophically based on the interpretivist paradigm and was conducted with an inductive approach. It is also applied - developmental in terms of purpose and descriptive in terms of method and data collection timeframe. A nested research design was used to achieve the research objective. The research participants included theoretical experts (professors of financial management and accounting) and empirical experts (managers of startup companies). Theoretical sampling was used until theoretical saturation was reached, and eventually, 24 individuals participated in the study. Data collection tools included semi-structured interviews and a questionnaire based on a decision matrix. The validity of the interview was confirmed based on four criteria: credibility, transferability, confirmability, and dependability. The reliability of the qualitative section was estimated to be favorable by calculating Holst's coefficient at 0.817 and Cohen's Kappa coefficient at 0.706. Data analysis was performed using grounded theory in the qualitative section and the SWARA method in the quantitative section. Based on the research model, it was determined that causal conditions (technical factors, managerial factors, governmental factors, and accounting factors) influence the core phenomenon (startup accounting). The core phenomenon, contextual conditions (financial business ecosystem infrastructure and financial business ecosystem financial resources), and intervening conditions (financial business ecosystem regulations) influence strategies and actions (technological strategy and financial strategy). This research provides a comprehensive model for understanding startup accounting within the financial business ecosystem. The findings highlight the critical influence of technical, managerial, governmental, and accounting-related factors on core accounting practices. Furthermore, the interplay between these practices, the broader ecosystem infrastructure, financial resources, and the regulatory framework shapes startups' strategic decisions (both technological and financial). These strategic choices, in turn, directly affect both the financial and non-financial performance outcomes of these nascent firms. The model underscores the need for a nuanced approach to startup accounting that takes into account the specific context of the financial business ecosystem.

Although previous research has examined the application of artificial intelligence (AI) across various areas of finance, there remains limited empirical evidence regarding its impact on corporate financial asset allocation. This gap is particularly evident when considering the organisational capabilities that enable firms to utilise AI technologies effectively. In the rapidly evolving technological landscape, artificial intelligence (AI) has emerged as a pivotal force driving innovation and transformation within corporate financial management. By embedding AI into organisational processes, companies have fundamentally reshaped their financial decision-making frameworks. However, the exact mechanisms through which AI adoption shapes the allocation of financial assets are still not fully understood. This study examines how artificial intelligence (AI) technologies influence the allocation of financial assets within corporations, with a specific focus on the moderating influence of three dynamic organisational capabilities: absorptive capacity, innovation capability, and adaptability. Based on panel data collected from companies listed on the Tehran Stock Exchange between 2020 and 2024, AI adoption is measured through textual analysis of management commentary reports obtained from the Codal system. The dependent variable comprises a set of financial ratios, including the proportion of financial assets relative to a firm’s total assets. The analysis employs multiple regression models with interaction terms to test the proposed hypotheses. Findings indicate that the adoption of AI substantially enhances the effectiveness of distributing financial assets. Moreover, absorptive capacity and innovation capability strengthen the association between AI adoption and the allocation of financial assets within firms' performance, while adaptability shows no statistically significant moderating effect. These results highlight the importance of both technological infrastructure and internal capabilities for leveraging advanced technologies to their fullest potential. This research not only enriches the academic discourse with fresh empirical insights but also offers valuable implications for financial managers, capital market regulators, and policymakers engaged in organisational digital transformation strategies.