درخت حوزه‌های تخصصی

This paper attempts to compare a Markov-Switching Dynamic Stochastic General Equilibrium (MS-DSGE) model by including deep habits consumption to a MS-DSGE model without deep habits. It is concluded that the deep habit adjusted model with regime switching is able to fit the Iranian data better. The results of estimating parameters indicate that deep habit formation, together with the persistence of habit stock, are significant parameters. The results also confirm that current and future consumption demand, expected marginal cost and stock of habits are effective driving forces in extracted New Keynesian Philips Curve considering deep habits. However, in contrast with Ravn et al (2006, 2010) findings, it is shown that presence of deep habit consumption in the model for Iranian economy, cannot lead to reduce inflation in response to monetary shock while the amount of increase in inflation in response to monetary shock in the model with deep habit is less than inflation increase in model without deep habits. Furthermore, in response to fiscal shock in the model considering deep habits, the negative effect of wealth could not be compensated in Iranian economy. Therefore, consumption begins to decrease in response to fiscal shock, although these reduction in the model without deep habits takes more longer than in the model with deep habits.

Digital banking (DB) is becoming a popular choice for many customers who value convenience and efficiency. However, it is also important to evaluate how well Internet banks perform in terms of customer satisfaction. This study aims to comprehensively identify and evaluate suitable indicators for DB implementation to promote its acceptance among users. Both financial institutions and customers perceive Internet banking services as more convenient compared to conventional branch-based services. We employed two techniques: The Importance-Performance Matrix and the Analytical Hierarchy Process. The Importance-Performance Matrix helped us rank the identified factors according to their importance and urgency for improving the bank’s performance. The analytical hierarchy process helped us compare and weigh the relative importance of various criteria and sub-criteria that affect the bank’s performance. According to our findings, the bank’s performance was influenced by five factors: ease of use, privacy, speed, human resources, and technology. We also discovered that ease of use and privacy were the most urgent factors that required immediate attention from the bank. Therefore, we advise that Tejarat Internet Bank's services focus on enhancing security and ease of use as their top priorities to improve their performance. This research introduces a novel perspective by switching the typical focus of online banking research from corporate entities to individual clients, this research offers an innovative point of view. Our investigation provides valuable insights useful for retail banking and banks that deal with corporate clients by extensively comparing and evaluating the unique requirements of these two consumer segments.

Making decisions regarding capital structure is among the most challenging issues ahead for firms and the most critical decisions for their survival. On the other hand, several significant aspects, such as behavioral factors, have been overlooked in this field. Thus, the present study mainly seeks to identify the factors affecting capital structure in Iranian firms, emphasizing the role of behavioral factors. The present study employs mixed qualitative and quantitative research methods. From the qualitative point of view, capital market experts were inquired, and theoretical saturation was achieved using the snowball method. After the interviews, research components were extracted through coding. The opinions of a group of experts and managers of firms listed on the Tehran Stock Exchange were used in the quantitative section, and a structural equation form was used to perform confirmatory factor analysis on the research model. A total of 63 concepts in the form of six categories were identified at the first stage, which was reduced to 58 in the form of six categories and was confirmed after the concepts were sent back to the experts. The principal components included behavioral factors, macroeconomic factors, political factors, socio-cultural factors, firm features, and corporate governance. Results were validated through factor analysis in the quantitative portion of the study. The present study can be considered among the comprehensive studies at the construct level with an integrated approach to firms' capital structure. The emergence of behavioral finance resulted from understanding the importance of measuring human behavior as a factor with transcendent consequences for financial decisions. Hence, most behavioral finance studies are focused on observable behaviors. However, the item response theory presents an integrated method for disciplines that work with cognitive variables. Accepting opportunities for new knowledge is essential for firm decisions to respond to the mental views of financial managers. The present study sought to identify the factors influencing firms' capital structure in Iran. The tool used in the present study reflected the elements making up the capital structure. In this regard, the notable point is how the classic criterion of structural capital components can explain financial managers' perception of decision-making. The research results in this area are interesting since we have confirmed a capital structure theory at the construct level. The conformity of the results and the obtained reliability levels indicate that this theory fits the given dimensions well. Moreover, relevant evidence indicates that senior financial managers adopt various states considering internal and external factors at the structural level, which can cause cognitive bias in decision-making.

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.