درخت حوزه‌های تخصصی

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.

Technical and fundamental analysis are the two principal methods for studying financial markets. However, access to internet and social media helps investors make better decisions. Social media has turned into a source of information for investors. Surowiecki (2005) found social media can predict better than individuals, known as the Wisdom of the Crowd. In this study, we tried to evaluate the wisdom of the crowd’s potential to improve stock price prediction accuracy. So, we developed a prediction model by Long Short-Term Memory based on the wisdom of the crowd. Persian users' opinions on Tehran Stock Exchange stocks were gathered for 8 months and classified as buying, sell, or neutral. During the research period, people mentioned 823 stocks and 52 stocks, which had over 100 recommendations, were chosen. Prediction model accuracy was increased for 19 stocks. While, for 33 stocks were not more accurate with the wisdom of the crowds and social media features. It is important to note that investors apply critical thinking. The wisdom of the crowd can be one input to the decision-making process, along with other related factors. The wisdom of the crowd provides an opportunity to access vast and diverse information. Getting opinions from various people can provide valuable insights into economics and investment preferences. The wisdom of the crowd can help reveal the flow of money. The combination of the wisdom of the crowd, fundamental, and technical analysis can be a useful tool for traders in detecting capital flow and profitable opportunities.

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.

Agriculture plays a pivotal role in Africa's development and is essential for achieving the Sustainable Development Goals (SDGs). However, the agricultural sector is inherently exposed to production risks, and many farmers in the developing world lack access to reliable agricultural insurance coverage. This situation arises due to limited data and knowledge about farmers' insurance needs and the high costs associated with insuring against severe agricultural risks. Promoting agricultural insurance as an instrument can have several significant impacts, aligning with multiple SDGs. It can help stabilize farmers' income, thereby contributing to the goal of reducing poverty (SDG 1). Moreover, agricultural insurance can provide a safety net for food producers, helping them manage the impacts of climate-related risks and aligning with SDG 13, which addresses climate action. Additionally, by enhancing the resilience of farmers and ensuring more predictable income, agricultural insurance can contribute to addressing hunger (SDG 2) and ultimately create a more sustainable and prosperous agricultural sector in Africa. Therefore, this study evaluated agricultural insurance as an instrument for sustainable food supply systems in Nigeria. This study adopted a survey design. This study captured thoughts, experiences, and observations of selected agricultural underwriters in the Nigerian insurance industry through structured questionnaire. A descriptive statistic was employed in the data analysis. This study results indicated that aside from farmers’ awareness which showed some level of yardstick with respect to farmers behavioural metrics, all other metrics played no significant roles. It was also recorded that why farmers’ age, gender, family size and farming experience have no significant roles in the uptake of agricultural insurance, all other participatory factors have major effects. The study contributed significantly to knowledge with the graphical representations of the challenges confronting the agricultural insurers in Nigeria. The study provided suitable recommendations that endear achievable SDGs in Nigeria.