The study aimed to analyze the effect of the capital structure of the corporate and its interference in corporate social responsibility activities on the firms corporate value during the time period from 2014-2017 the study tested the effect of financial leverage and CSR on the corporate value of the firms by testing 17 companies registered in the indicator of CSR in the Egyptian stock market through applying panel data analysis. After testing the effect of both variables together the study found that there is significant effect of financial leverage on corporate value while applying CSR activities has no effect on the corporate values which means that there is a lack in the awareness of investors about the importance of applying CSR activities in Egypt.
Financial management has two main objectives profit and wealth maximization, well organized management of WC components should contribute to the achievement of these objectives. This study clarified the factors which affect WCM, which consequently will affect the business health as a whole and this will influence corporate ’performance and its corporate value. The study will examine the relation between firms profitability and its corporate value by applying panel data analysis on16 companies registered in the Egyptian stock market during the period from 2013 to 2017.The performance of companies is measured through profitability using return on assets (ROA) and firms value were measured by Tobin’s Q (TQ) ratio. The working capital management was measured by using current assets ratio (CAR), quick ratio (QR) and cash ratio (CR).
This study investigates the effectiveness of technology models in credit risk scoring modeling in emerging markets. the study proposes evaluation methods for credit risk scoring modeling for current and potential borrowers through an investigation into the Egyptian banking industry by offering and examining a framework for the integration of big data and artificial neural networks based on systematic and unsystematic risk for both the macroeconomic environment and characteristics of current and potential borrowers. The data for the borrowers under examination covers the period from 2015 to 2019 for 75 firms, excluding 2020 and 2021 data to isolate the impact of COVID-19 on the results of the inferred statistics. Artificial Neural Networks was training within 25 firms under NeuroXL program but examination for 50 firms. The study found the ability of artificial neural networks to rank the commitment of borrowers in Egyptian banks under big data about the firm and Egyptian economy. Additions to discrepancy between the proposed model against some traditional models. Finally; The Integration of Big Data and ANN can help banks to bring out the value of data within create a level of financial stability for banks. Especially in emerging markets characterized by information inefficiency.
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