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This study investigates the relationship between firm value creation (FVC) and corporate environmental and social disclosure (CESD) and how FVC differs in terms of its life cycle stages in Egypt by analyzing the impact on FVC from 2013 to 2019 of non- inancial firms listed on the Egyptian Stock Exchange (EGX). The study used multivariate analyses and found that CESD negatively impacts FVC. Furthermore, FVC differs in its life cycle stages. The results proved that the growth stage negatively affects the FVC, and the shaking-out stage positively affects the FVC. Meanwhile, the remaining stages show no significant relationship with FVC. For the control variables, industry type, liquidity, auditor type, leverage, and profitability showed significant positive influences on FVC. At the same time, the firm age and the board size exhibited a significant negative association with FVC. Meanwhile, the firm size showed no significant association. These findings offer insight into the factors influencing FVC for non-to the current literature because empirical research on the influence of CESD and different life cycle stages on FVC is insufficient in Africa, especially in Arabic nations. Our results have some consequences for the business strategy, as strong CESD can enhance a firm’s reputation and attract environmentally and socially conscious consumers, potentially leading to increased sales and customer loyalty. This, in turn, can contribute to FVC through higher profits and a stronger brand image. CESD can also mitigate environmental risks and improve resource efficiency, reducing costs and boosting profitability, ultimately impacting FVC. Our results greatly interest suppliers, creditors, investors, and researchers in sustainability, CESD, and corporate voluntary disclosure.

Keyword: Firm Value Creation, Corporate Environmental and Social Disclosure, Life Cycle Stages, non-financial firms, Egyptfinancial Egyptian firms listed on the EGX. This study adds

Machine learning offers the potential to revolutionize financial fraud detection, providing a powerful alternative to the limitations of traditional financial ratio analysis. By employing panel data derived from financial statements spanning from 2017 to 2022, this study investigates the feasibility of implementing these methodologies to augment the efficacy of fraud detection systems specifically tailored to the Egyptian stock market. In contrast to conventional financial ratio analysis, the applied techniques evaluate and contrast three sophisticated machine learning algorithms—namely logistic regression (LR), support vector machine (SVM), and XGBoost. The findings demonstrate a notable disparity in performance when comparing machine learning methods to conventional methodologies using performance metrics indicators, specifically recall, Fi-score, and precision, implying that machine learning can provide more precision. Furthermore, this research indicates that XGBoost routinely exhibits superior performance compared to the alternative approaches in critical fraud detection measures. In brief, the researchers analyzed the ramifications of these findings for accountants and auditors in Egypt, underscoring the importance of employing a sophisticated methodology that integrates machine learning with expert opinion and ample comprehension of the financial reporting environment in Egypt in order to optimize the efficacy of fraud detection. Ultimately, it is the responsibility of auditors and accountants to thoroughly evaluate and select machine learning methods that are ideal in alignment with their specific data characteristics, risk tolerances, and transparency requirements. Furthermore, it is crucial to underline the significance of audit quality: auditors must be aware of how audit quality indicators, such as audit tenure and productivity, might influence the identification of fraudulent activities. Higher risk locations may be acknowledged by these indications, necessitating a more thorough analysis and the use of more sophisticated analytical tools. This research explores how Machine Learning (ML) can empower auditors in fraud detection. The paper recommends utilizing unsupervised ML to identify anomalies in vast datasets, flagging unusual patterns for investigation. By embracing these ML techniques, auditors can pave the way for a more data-driven and efficient approach to uncovering fraudulent activities.

Keywords: Fraud detection- machine learning- financial ratio detection- logistic regression- support vector- XGBoost.

Financial ratios have been used for a long time to evaluate a company’s performance and financial status. These have also been used as indicators of corporate failure in an increasing number of studies since the turn of the century. However, scholars have not given this relationship among Philippine enterprises much thought. Therefore, the purpose of this study was to ascertain, for the years 2015–2019, how financial ratios affected the degree of financial distress of publicly listed industrial enterprises in the Philippines. The dependent variable in this study was the degree of financial distress as determined by the Altman (2005) Emerging Markets Scoring (EMS) Model, whereas the independent variables were the financial ratios of the enterprises. From 2015 to 2019, information from 51 publicly listed industrial companies was sourced from the Orbis Asia Pacific database and the PSE Edge website. According to the descriptive statistical analysis, 47.5% of the firmyear observations relate to businesses that are in financial hardship. When businesses transition from being financially stable to being in financial distress, their overall state gets worse. The panel data regression analysis revealed that the interest coverage ratio, retained earnings to total assets ratio, debt-to-assets ratio, liquidity ratios, and asset turnover ratio were statistically significant predictors of the subject enterprises’ degree of financial distress. In order to preserve or raise their degree of financial stability, the business should focus on certain areas, as this study’s actual data supports. This report also highlights how crucial it is to carefully examine the financial standing of companies in order to implement investor protection measures.

Keywords: Financial Ratios, Financial Distress, Altman Emerging Markets Scoring (EMS) Model, Panel Data Analysis

Audit opinion plays a crucial role in influencing the decisions of various stakeholders. Previous studies have explored the factors impacting an auditor’s opinion regarding the financial statement presentation. This study aims to investigate the influence of audit quality and ownership concentration on audit opinion in Egypt. Utilizing data from 438 firm-year observations from the Egyptian Stock Exchange (EGX) between 2015 and 2019, the regression outcomes indicate that both audit quality and ownership concentration significantly contribute to the issuance of clean audit opinions. These findings are particularly valuable to investors, creditors, company executives, auditors, and researchers engaged in corporate governance and auditing studies.

Keywords: Audit Opinion, Ownership Concentration, Audit Quality, Egypy

This study looks into how the effectiveness of the audit committee affects financial reporting quality (FRQ) in the Egyptian context and offers more information on how the qualities of the audit committee affect financial reporting quality. The analysis makes use of secondary data from 327 observations covering a sample of 48 companies that were listed between 2016 and 2022 on the Egyptian Stock Exchange. The size, frequency of meetings, gender, and independence of the audit committee serve as the independent variable’s proxies for the audit committee’s effectiveness (ACE). Earnings Management (EM) is used to measure the dependent variable, which is FRQ.. The basis for debate is provided by descriptive data, panel-corrected standard error (PCSE) and generalized least squares (GLS) regression models, audit committee features, and potential connections to the caliber of financial reporting. The findings showed that while there is no correlation between FRQ and either AC size or AC gender diversity, there is a positive and substantial association between AC meetings, AC independence, and FRQ. The results imply that a few key attributes of the audit committee might be crucial in limiting the committee’s inclination to participate in earnings management.

Keywords: Audit Committee size; Audit Committee Meetings, Audit Committee Gender; Audit Committee Independence; Financial Reporting Quality; Earnings Management; Egypt