This empirical research endeavor seeks to enhance the accuracy of forecasting time series data in the banking sector by utilizing data from the Amman Stock Exchange (ASE). The study relied on daily closed price index data, spanning from October 2014 to December 2022, encompassing a total of 2048 observations. To attain statistically significant results, the research employs various mathematical techniques, including the non-linear spectral model, the maximum overlapping discrete wavelet transform (MODWT) based on the Coiflet function (C6), and the autoregressive integrated moving average (ARIMA) model. Notably, the study’s findings encompass the comprehensive explanation of all past events within the specified time frame, alongside the introduction of a novel forecasting model that amalgamates the most effective MODWT function (C6) with a tailored ARIMA model. Furthermore, this research underscores the effectiveness of MODWT in decomposing stock market data, particularly in identifying significant events characterized by high volatility, which thereby enhances forecasting accuracy. These results hold valuable implications for researchers and scientists across various domains, with a particular relevance to the fields of business and health sciences. The performance evaluation of the forecasting methodology is based on several mathematical criteria, including the mean absolute percentage error (MAPE), the mean absolute scaled error (MASE), and the root mean squared error (RMSE).

This study examined the company’s characteristics that affect the capital structure of insurance companies in Jordan. The study has employed panel regression model in investigating the capital structure of insurance companies using financial statements data of 23 companies covering the period 2010-2014. The results showed that both the static trade-off and pecking order theories are important in explaining the capital structure of insurance companies in Jordan. Company’s characteristics: size, profitability, tangibility, growth and risk were statistically significant to capital structure. Based on multiple and single regression the results of the study showed a statistical significant relationship between characteristics of insurance companies and their capital structure. The results also revealed a significant negative relationship between capital structure and company’s size, profitability, growth and risk while tangibility was significantly positively correlated to capital structure.

This study uses historical data and modern statistical models to forecast future Gross Domestic Product (GDP) in Jordan. The Wavelet Transformation model (WT) and Autoregressive Integrated Moving Average (ARIMA) model were applied to the time series data and yielded a best-fitting result of (2,1,1) for estimating GDP between 2022-2031. The study concludes that GDP is expected to increase with a positive growth rate of around 3.22%, and recommends government agencies to monitor GDP, strengthen existing policies, and adopt necessary economic reforms to support growth. Additionally, the private sector is encouraged to enhance production tools to achieve economic growth that benefits all sectors of society.

This study examines the effectiveness of Artificial Intelligence (AI) techniques in detecting financial fraud, while also addressing the key challenges associated with their implementation and use. It presents a systematic review of peer-reviewed literature to evaluate how AI-driven approaches particularly machine learning (ML) and deep learning perform in fraud detection within big data environments. The findings reveal that AI-based systems significantly enhance fraud detection capabilities by enabling real-time analysis and demonstrating greater adaptability to evolving fraud patterns compared to traditional rule-based systems. However, several challenges hinder their optimal performance, including data privacy concerns, vulnerabilities in electronic systems, ethical considerations, and potential algorithmic bias. Additionally, issues related to scalability limit the ability of small and medium-sized enterprises to fully benefit from AI technologies. Despite these constraints, AI remains a powerful tool in combating financial fraud. Addressing these challenges requires improvements in data quality, increased transparency in AI models, and stronger cybersecurity frameworks. Moreover, effective collaboration between policymakers and industry stakeholders is essential to develop regulatory and ethical guidelines that ensure the responsible and efficient use of AI in fraud detection.

This paper analyzes the impact of financial performance reported data on market value. The study was conducted on electricity companies for the period 2011 to 2021. The study also empirically analysis the intervening role for oil and gas as operating costs on this relationship. The empirical analysis involved testing the direct effect for company size (TA), profitability (ROE), debt (DR), liquidity (CR) and oil and gas price (OG-P) as independent variables on market value measured by price to book value (P-BV) as dependent variable. Several statistical test were used in the study comprise correlation, simple and multiple regression through Ordinary Least Square (OLS) in order to verify the effect of independent and mediating variables on the dependent variable. The main findings based on correlation, simple and multiple regression can be summarized as follows: independent variables namely ROE, CR and the mediating variable OG-P were found statistically significant and justified the change in market value; other factors TA and DR failed to prove any effect on market value of companies. The oil and gas costs confirmed their impact on market value as single variables effect and joint variable effect.

This study aims to compare the stability and presentation of revenues under International Financial Reporting Standard (IFRS) 15 in two key Jordanian sectors, industry and services. The objective is to determine the impact of transitioning from IAS 18 to IFRS 15 on revenue reporting practices in these sectors. To achieve the research objective, the study analyzed the annual operating revenue of selected companies in the industrial and services sectors over the period from 2013 to 2020. This period covers both the pre-implementation phase under IAS 18 and the post-implementation phase under IFRS 15. Quantitative analysis was conducted to examine differences in revenue stability and presentation before and after the adoption of IFRS 15, with statistical tests applied to evaluate the significance of these changes. The results indicate a positive statistical significance in the stability and presentation of operating revenues in the services sector following the adoption of IFRS 15, suggesting improved consistency and clarity in revenue reporting. Moreover, IFRS 15 had a notable effect on the value of reported revenues in Jordanian services firms. In contrast, the industrial sector showed a negative statistical significance in revenue stability and presentation when comparing the periods under IAS 18 and IFRS 15, indicating potential challenges or inconsistencies in applying the new standard within this sector. The implementation of IFRS 15 has had varying impacts on revenue reporting across different sectors in Jordan. While the services sector benefited from increased revenue stability and clearer presentation, the industrial sector experienced a decline in reporting stability, highlighting sector-specific differences in adapting to the new standard. These findings have important implications for regulators, auditors, and financial statement preparers. The results suggest a need for tailored guidance and training for industrial sector entities to better align with IFRS 15 requirements. Additionally, policymakers may consider sector-specific support to facilitate smoother transitions when future accounting standards are introduced.