Journal Article10.1080/15228916.2020.1826856
An Analysis of Financial Statement Manipulation among Listed Manufacturing and Trading Firms in Ghana
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TL;DR: In this article, the likelihood of financial statement manipulation among 19 listed manufacturing and trading firms on the Ghana Stock Exchange for the period 2008 to 2017 was analyzed using the Beneish model.
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Abstract: We analyze the likelihood of financial statement manipulation among 19 listed manufacturing and trading firms on the Ghana Stock Exchange for the period 2008 to 2017. We use the Beneish model to gr...
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Citations
A Statistical Model of Fraud Risk in Financial Statements. Case for Romania Companies
Andrada-Ioana Sabău (Popa),Codruța Mare,Ioana Lavinia Safta +2 more
- 01 Jun 2021
TL;DR: In this article, the authors identify which of the eight-variables from the Beneish influences the most or least the outcome of the final score, as a percent, by developing a statistical model, which is validated with only 10% of the non-fraud companies being mistakenly considered as fraud based on their model and vice versa.
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Earnings manipulation behavior in the banking industry of Bangladesh: the strategical implication of Beneish M-score model
TL;DR: In this paper , the authors used the likely and non-likely manipulator Beneish model to determine the number of companies involved in earnings manipulation and empirically investigated the common manipulation items among the companies.
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Financial Statements Fraud of Banks and other Financial Institutions in Nigeria
TL;DR: The study investigates financial statement fraud of banks and other financial institutions in Nigeria and finds that 26.67% (23.33%) of firms likely manipulate financial books. Sales in receivable, sales growth, depreciation expenses, leverage and accruals to assets ratios are significant predictors of the probability of manipulations.
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Using the Beneish M-score Model to Detect Financial Statement Fraud in the Microfinance Industry in Ghana
TL;DR: In this article , the effect of corporate earnings manipulation on micro-finance institutional failures in Ghana was investigated by employing a quantitative investigative technique to analyse data obtained from the Bank of Ghana (BOG) on microfinance companies covering eight-year intervals.
Gender Inclusive Intermediary Education, Financial Stability and Female Employment in the Industry in Sub-Saharan Africa
TL;DR: In this paper, the authors examined how financial stability modulates the effect of inclusive intermediary education on female employment in the industry for the period 2008-2018 in Sub-Saharan Africa.
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TL;DR: In this article, the authors test whether firms that would benefit from import relief attempt to decrease earnings through earnings management during import relief investigations by the United States International Trade Commission (ITC).
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Baruch Lev,S. Ramu Thiagarajan +1 more
TL;DR: In this article, the authors identify below a set of financial variables (fundamentals) claimed by analysts to be useful in security valuation and examine these claims by estimating the incremental value-relevance of these variables over earnings.
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The Detection of Earnings Manipulation
TL;DR: In this paper, a profile of a sample of earnings manipulators, their distinguishing characteristics, and a suggested model for detecting manipulation is presented; the model's variables are designed to capture either the financial statement distortions that can result from manipulation or preconditions that might prompt companies to engage in such activity.
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Data Mining techniques for the detection of fraudulent financial statements
TL;DR: This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS.
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Detection of financial statement fraud and feature selection using data mining techniques
P. Ravisankar,Vadlamani Ravi,G. Raghava Rao,Indranil Bose +3 more
- 01 Jan 2011
TL;DR: Data mining techniques such as Multilayer Feed Forward Neural Network, Support Vector Machines, genetic programming, Genetic Programming, Group Method of Data Handling, Logistic Regression, and Probabilistic Neural Network are used to identify companies that resort to financial statement fraud.
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