The objective of this study is to examine the influence of profitability and asset management on the valuation of a firm. The data utilized in this study was sourced from the financial statements of industrial firms that are publicly traded on the Amman Stock Exchange. The fixed effects model was utilized for all four economics models in the analysis of the panel data. One of the principal discoveries of this investigation is the favorable impact of the profitability and asset management metrics on the valuation of a corporation. The research findings indicate that the inclusion of dividend payments as a moderating factor in the association between profitability and asset management results in an augmentation of a firm's worth. The positive correlation between dividend payouts and a company's worth is attributed to their link with profitability and asset management. This study has the ability to offer valuable insights into corporate management by examining the aspects that can enhance a company's value. Moreover, it holds significance for both existing and potential investors to discern the determinants that have the potential to augment the worth of a corporation.

The assessment of total risk-weighted assets (LTRWAs) in the banking sector is of the utmost importance. It serves as a critical component for regulatory compliance, risk management, and capital adequacy. By accurately assessing LTRWAs, banks can effectively meet regulatory requirements, efficiently allocate capital resources, and proactively manage risks. Moreover, the accurate assessment of LTRWAs supports performance evaluation and fosters investor confidence in the financial stability of banks. This study presents statistical analyses and machine learning methods to identify factors influencing LTRWAs. Data from Bahrain, Jordan, Qatar, the United Arab Emirates, and Yemen, spanning from 2010 to 2021, was utilized. Various statistical tests and models, including ordinary least squares, fixed effect, random effect, correlation, variance inflation factor, tolerance tests, and fintech models, were conducted. The results indicated significant impacts of the unemployment rate, inflation rate, natural logarithm of the loan-to-asset ratio, and natural logarithm of total assets on LTRWAs in regression models. The dataset was divided into a training group (90% of the data) and a testing group (10% of the data) to evaluate the predictive capabilities of various fintech models, including an adaptive network-based fuzzy inference system (ANFIS), a hybrid neural fuzzy inference system (HyFIS), a fuzzy system with the heuristic gradient descent (FS.HGD), and fuzzy inference rules with the descent method (FIR.DM) models. The selection of the optimal model is contingent upon assessing its performance according to specific error criteria. The HyFIS model outperformed others with lower errors in predicting LTRWAs. Independent t-tests confirmed statistically significant differences between original and predicted LTRWA for all models, with HyFIS showing closer predictions. This study provides valuable insights into LTRWA prediction using advanced statistical and machine learning techniques, based on a dataset from multiple countries and years.

Over the past few decades, financial performance has attracted researchers’ attention, especially in the insurance sector. Insurance is a tool for the growth and sustainability of both rising and developing economies. It promotes economic stability for people, organizations, and governments by taking on risk and spreading it across the market. We intend to classify insurance companies’ financial performance in Jordan’s Amman Stock Exchange (ASE). The sample size is 15 out of 22 selected insurance firms from 2008 to 2020. We apply the Multi-Layer Perceptron (MLP) model for the detection of (high/low) total asset turnover (TAT) as output, while we select the subrogation (SB), claims paid (CP), market capitalization (MC), and total shareholders’ equity (SE) as input to the MLP model. The performance of the MLP model is evaluated using different criteria, namely the false positive rate (FP rate), false negative rate (FN rate), F-measure, precision, and accuracy (ACC). The results show that MLP is efficient and performs well in multiple criterion tests through iteration growth. Based on our knowledge, the paper assesses the financial performance of Jordanian insurance firms, which has not been investigated previously. Furthermore, this study gives valuable information to regulators and policymakers to improve asset management efficiency in the insurance sector.

Due to the great importance of the financing structure of banks, the impact of capital structure on the financial performance of banks listed on the Amman Stock Exchange has been examined. To achieve the objectives of this study, we have followed the experimental approach. The study relied on financial variables. The Capital Structure has been measured by the ratios of total debt to total assets and total debt to total equity. Both ratios are independent variables. The dependent variable in this study is the financial performance of banks represented by the ratio of return on assets, the ratio of return on equity, the ratio of return on investment, and the ratio of return on share. The study community and sample consisted of twelve commercial banks listed on Amman Stock Exchange (ASE) during the period (2007-2017). Statistical Package for the Social Sciences (SPSS) software was used in testing of research hypotheses. The most important results are that the capital structure has an impact on return on assets (ROA), while it has no impact on return on equity (ROE), return on investment (ROI) and earnings per share (EPS) in Jordanian commercial banks.

The study aims to examine the effects of artificial intelligence (AI) on the consistency and analysis of financial statements in hotels in ASEZA, Jordan. This research is an exploratory, empirical study, which uses the methodology of data collection and interpretation to draw conclusions. The researchers used the arithmetic mean, standard deviation, T-test and ANOVA test to calculate the degree of significance of the study questions. The findings of a basic linear regression study of the impact of AI implemented in Jordanian hotels on the integration of accounting information systems and the association between AI and the integration of accounting information systems (R = 59.6%) also indicate that the fixed limit value amounted to (2.060) and the value of (Beta) for T-test