Investment decision making among gulf investors: Behavioural finance perspective

The rationality hypothesis has been a very popular topic among the academics. Being a widely accepted hypothesis as part of the traditional finance theories, an investor is deemed a rational agent and makes rational decisions by exhausting all available alternatives.However, recently, new behavioural finance theories have been gaining ground as many empirical findings, which have been left unanswered by the traditional theories, can be explained by these behavioural-approach based theories. This research examined the impact of psychological factors on risk-taking behaviour in investment decisions. In particular, this research considered the possible effects of psychological factors, namely herding, heuristics, prospect, market, self-attribution bias, and familiarity bias, in making investment decisions. The findings in this paper declared that risk-taking behaviour in investment is affected by herding factors, heuristics factors, prospect factors, market factors and self-attribution bias factors. The familiarity bias factors do not significantly affect risk-taking behaviour in financial investment

The impact of domestic savings gap on the current account balance in Jordan during the period (1995-2020)

The study aims to demonstrate the role of the domestic savings gap in the Current Account (CA) balance in Jordan, by analyzing the size and development of the domestic savings gap in Jordan. As well as analyzing the role of domestic savings in the CA, and balance in Jordan. Auto Regressive Distributed Lag (ARDL) and Bound Testing methodology were used to measure the short and long-term impact of deficit determinants in the CA of the Jordanian balance of payments. Several results were found in the current study. First, a positive and significant effect of the public savings gap on the CA deficit in Jordan was found during the period 1995 to 2020. Second, a positive and significant effect of the private sector savings gap on the CA in Jordan during the study period. Finally, the government sector’s gap has a greater impact compared to the private sector's gap on the CA in Jordan. The study recommends the necessity of drawing up incentive policies for domestic savings and creating incentives and means that can help increase the mobilization and distribution of savings to finance productive investments to reduce the CA deficit in Jordan

The role of information technology in raising the efficiency of Amman stock exchange mediated by the behavior of the stock prices

The study aimed to explore the role of information technology in raising the efficiency of the Amman Stock Exchange, mediated by the behavior of the stock prices. The study chose a sample consisting of 24 companies that are listed on the Amman Stock Exchange. The study used the average of the abnormal return of the stocks gained by companies through information technology applications. The study carried out a multiple regression analysis to explore the degree to which the independent variable affected the dependent one. The study results found that the abnormal return of the stocks gained by companies through information technology applications is low. The study also found that there is a significant relationship between using IT applications and the efficiency of the Amman Stock Exchange, mediated by the behavior of the stock prices. Therefore, the study recommends expanding the scope of using IT in emerging stock markets, including the Amman Stock Exchange, with the aim of raising the operational efficiency of such markets

Forecasting the Number of Traffic Accidents in Jordan using the Poisson Regression Model

The study aims at forecasting the number of traffic accidents in Jordan for the year 2022, based on the monthly data related to traffic accidents for the period (2017–2021) using the Poisson regression model. SPSS version 26 and Minitab version 19 data analysis programs were used to analyze the collected data. The study concluded that the use of the Poisson regression model is very appropriate to forecast the number of traffic accidents during the next period of time. The Poisson regression method is a useful method for estimating and forecasting. The researchers recommend adoption of this technique in related studies, conducting more extensive studies on the Poisson regression model, and reconsidering the current legislation and the penalties related to traffic accidents

The Impact of Intellectual Capital on Operational Performance in Jordanian Service Companies: Evidence from the Amman Stock Exchange

Purpose: This study seeks to determine how intellectual capital (IC) affects the operational effectiveness of service companies listed on the Jordanian Amman Stock Exchange (ASE).   Theoretical framework: The study is based on the concept of intellectual capital, which encompasses human capital, structural capital, and relational capital. These dimensions are examined to understand their influence on operational performance.   Design/Methodology/Approach: The study population consists of 40 service companies listed on the ASE, with a sample of 22 companies selected for analysis. Data collection relied on secondary sources, including reports and bulletins issued by the ASE between 2017 and 2021. Regression analysis is used in the study to look at the connections between several operational performance metrics and intellectual capital.   Findings: The findings reveal that human capital and structural capital have a significant positive impact on operational flexibility. Human capital and relational capital significantly influence cost efficiency. Structural capital, company size, and company age show significant effects on the inventory turnover rate. Human capital positively affects the asset turnover rate, while structural and relational capital do not exhibit significant effects.   Research, Practical & Social implications:  This study offers insightful information about the connection between intellectual property and operational effectiveness in service businesses listed on the ASE. The findings have practical implications for enhancing operational capabilities and efficiency within these organizations. Additionally, the study contributes to the existing knowledge on intellectual capital's impact on operational performance and fills a gap in the understanding of this relationship in the Jordanian context.   Originality/Value:  This study is one of the first to investigate how intellectual capital affects the performance of operational aspects in service businesses listed on the ASE in Jordan. By focusing on the unique characteristics of the Jordanian market, the study adds to the body of knowledge and advances knowledge of the function that intellectual capital plays in influencing operational success

An Efficient Method for Pricing Analysis Based on Neural Networks

The revolution in neural networks is a significant technological shift. It has an impact on not only all aspects of production and life, but also economic research. Neural networks have not only been a significant tool for economic study in recent years, but have also become an important topic of economics research, resulting in a large body of literature. The stock market is an important part of the country’s economic development, as well as our daily lives. Large dimensions and multiple collinearity characterize the stock index data. To minimize the number of dimensions in the data, multiple collinearity should be removed, and the stock price can then be forecast. To begin, a deep autoencoder based on the Restricted Boltzmann machine is built to encode high-dimensional input into low-dimensional space. Then, using a BP neural network, a regression model is created between low-dimensional coding sequence and stock price. The deep autoencoder’s capacity to extract this feature is superior to that of principal component analysis and factor analysis, according to the findings of the experiments. Utilizing the coded data, the proposed model can lower the computational cost and achieve higher prediction accuracy than using the original high-dimensional data

An Efficient Method for Pricing Analysis Based on Neural Networks

The revolution in neural networks is a significant technological shift. It has an impact on not only all aspects of production and life, but also economic research. Neural networks have not only been a significant tool for economic study in recent years, but have also become an important topic of economics research, resulting in a large body of literature. The stock market is an important part of the country’s economic development, as well as our daily lives. Large dimensions and multiple collinearity characterize the stock index data. To minimize the number of dimensions in the data, multiple collinearity should be removed, and the stock price can then be forecast. To begin, a deep autoencoder based on the Restricted Boltzmann machine is built to encode high-dimensional input into low-dimensional space. Then, using a BP neural network, a regression model is created between low-dimensional coding sequence and stock price. The deep autoencoder’s capacity to extract this feature is superior to that of principal component analysis and factor analysis, according to the findings of the experiments. Utilizing the coded data, the proposed model can lower the computational cost and achieve higher prediction accuracy than using the original high-dimensional data