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Ibrahim El-Sayed Ebaid

Researcher at Umm al-Qura University

Publications -  17
Citations -  1081

Ibrahim El-Sayed Ebaid is an academic researcher from Umm al-Qura University. The author has contributed to research in topics: Earnings & Chief audit executive. The author has an hindex of 9, co-authored 16 publications receiving 957 citations. Previous affiliations of Ibrahim El-Sayed Ebaid include Tanta University.

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The impact of capital‐structure choice on firm performance: empirical evidence from Egypt

TL;DR: In this article, the impact of capital structure choice on firm performance in Egypt as one of emerging or transition economies is investigated using multiple regression analysis in estimating the relationship between the leverage level and firm's performance.
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Accruals and the prediction of future cash flows

TL;DR: In this paper, the authors examined the comparative abilities of current period cash flows and earnings (and its components) to predict one-year-ahead cash flow from operations in Egypt.
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Internal audit function: an exploratory study from Egyptian listed firms

TL;DR: In this paper, the authors explore the nature and characteristics of internal audit function in Egyptian listed firms and assess its ability to fulfill its role in corporate governance, and reveal that internal audit functions have low levels of organizational independence, management support, and qualification of internal auditors.
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Corporate governance practices and auditor's client acceptance decision: empirical evidence from Egypt

TL;DR: In this paper, the authors examined the impact of corporate governance practices on auditors' decisions (judgments), especially, the client acceptance decision in Egypt, where corporate governance is neither mandatory nor legally binding.
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The value relevance of accounting‐based performance measures in emerging economies

TL;DR: In this paper, the relative and incremental value relevance of a set of accounting-based measures of firm's performance in the emerging capital market of Egypt was examined and compared using OLS regression models.