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The purpose of this paper is to start with the background of the construction of the M-score model, find the variables that can represent the fraud characteristics of Chinese companies, and use the data of Chinese A-share listed companies to modify the M-score model.

In this paper, the fraud behavior of Chinese enterprises that M-score cannot detect is summarized as the basis of adding variables. Then, based on the data of Chinese listed companies, a modified M-score model including nine variables is constructed by the logistic regression method based on Wald.

Based on the original 8 variables of M-score, this paper adds 10 new variables that can represent the fraud characteristics of Chinese listed companies, and finally, constructs a modified M-score model with 9 variables. Results indicated that indexes such as gross profit margin, fixed assets depreciation rate, equity concentration and audit opinion can characterize the financial fraud of Chinese listed companies.

The modified M-score model based on the characteristics of Chinese enterprises’ fraud is more suitable for Chinese market, which can help investors avoid fraud risks, protect their own rights and interests and reduce losses.

Starting from the background of the model, this paper looks for variables that can characterize the characteristics of fraud in Chinese listed companies. Then, subdivides the research samples into specific fiscal years in which fraud occurs, so that the modified M-score model can be more suitable for the Chinese market.

The aim of this paper is to detect whether there are companies listed in the general index of Athens Stock Exchange Market that possibly conduct earnings manipulation during 2017–2018.

The paper is based upon the Beneish model (M-score), which consists of eight variables to examine the probability of financial statement fraud related to earnings manipulation for 40 companies listed in the Athens Stock Exchange Market. Any company with an M-score −2.22 or above is likely to be a manipulator whereas any company that scores −2.22 or less is unlikely to conduct earnings manipulation.

After calculating the M-score for each company, it was found that 33 (out of 40) companies had M-score values lower than −2.22. Therefore, 82.5% of the sample is considered rather unlikely to conduct earnings manipulation whereas 17.5% of the companies listed in the general index of Athens Stock Exchange Market is likely to manipulate its earnings.

In this paper, all institutions related to financial services were left out of the sample because of the fact that M-score cannot provide reliable results when applied on similar companies.

Beneish model offers a probability of financial fraud and can be therefore used as a supplementary test for auditors, fraud examiners or even national regulators such as the Hellenic Accounting and Auditing Standards Oversight Board or the Hellenic Capital Market Commission. The results of this paper can contribute to the literature concerning financial fraud in Greece during 2017–2018 because no relevant recent researches have been published yet.

The increasing incidence of fraudulent financial reporting by firms in recent years raises concerns about investors' confidence in capital markets. Academicians and industry practitioners adopt diverse risk management techniques to detect fraudulent reporting of financial statements. This paper aims to determine the effectiveness of the Beneish M-score and Altman Z-score models for the early detection of material misstatements at Comscore, Inc., a media analytics firm in the United States of America.

The financial statements of Comscore Inc. from 2012 to 2018 were analyzed with the primary objective of early fraud detection by employing the Beneish M-score and the Altman Z-score.

The study’s outcomes indicate that the Beneish M-score is less predictable in fraud detection compared to the Altman Z-score. The study results did not confirm the efficacy of the Beneish model in predicting fraudulent financial statements. The study concludes that the choice of forensic tool greatly influences fraud detection outcomes.

The research findings can guide the policy decision-making of investors, financial auditors, and forensic auditors as this study provides some evidence of the effectiveness of forensic tools in the detection of financial statement fraud in corporate entities.

This is the first study to apply these two widely used tools to the most recent big corporate scandal: Comscore, Inc.

The purpose of this paper is to outline three analytic tools utilized in the analysis and interpretation of LibQUAL+™ quantitative data.

D‐M scores, value rankings, and split‐file cross‐tabulations were used to assess the service items from the 2004 LibQUAL+™ quantitative data. The D‐M score is methodologically superior to other methods used in that it is a single score that takes into account all three LibQUAL+™ perception/expectation scores as dictated by the theoretical model LibQUAL+™ is based upon.

The paper finds that these tools provide a way to more easily utilize LibQUAL+™ results in taking actions and developing strategic plans designed to improve patrons' perceptions of service quality. These tools also allow for the continuous evaluation of implemented plans.

The paper discusses how these tools helped produce findings that were informative and in a format that decision makers could easily comprehend and utilize.

This paper outlines three approaches and offers practical recommendation of how to analyze and interpret LibQUAL+™ quantitative data as well as present findings to strategic stakeholders.

This study aims to determine the number of companies involved in earnings manipulation. Additionally, this study has empirically investigated the common manipulation items among the companies.

Bangladesh's listed commercial banks are selected as a sample for this study, and financial data from 2009 to 2018 were collected. The likely and nonlikely manipulator Beneish model (1999) divides the sample into two groups. Based on the M-score of the model, the banks are put into two groups. To identify the most influential variables, an independent sample t-test was done with the help of Statistical Package for Social Sciences (SPSS).

The findings show that banks in Bangladesh have an unstable trend in making manipulated financial reports. Results of the t-test reveal that overstating revenues, increasing intangible assets, lessening cost and accruals are the most appealing items for preparing a fraudulent financial report. The findings of this research work will help the investors take the right decision having the idea of manipulation in the banking sector of Bangladesh.

In the presence of many irregularities in the banking sector Bangladesh, very few studies have been carried out in forensic accounting and fraudulent financial reporting practices. Much research has focused on earnings management techniques. This research specifically focuses on identifying earnings manipulation in financial statements for micro-level variables like accounting accruals, intangible assets, etc. This will help policy-makers and financial statement readers to be proactive while reading financial statements and taking any investment decision.

The purpose of this study is threefold: first, to detect trends in financial statement manipulation; second, to measure the level of manipulation and to measure the variation in manipulation between countries; and, third, to identify widely used techniques in financial statements manipulation.

This study uses financial data of listed companies from Asia, namely, Japan, Singapore, Malaysia, Indonesia, Thailand, Hong Kong and China. The study adopts financial ratios, financial forensic tool, dichotomous approach and statistical tools to analyze the data (84,000 observations) over a period of four years from 2010 to 2013.

The results show that 34 per cent of sample companies in selected Asian countries are involved in the manipulation of financial statements; the average level of manipulation (overall manipulation index) is 72 per cent; and there is a significant difference between countries at 5 per cent level. The study also identifies four most commonly used techniques, namely: days’ sales in receivable (DSRI), depreciation (DEPI), assets quality (AQI) and total accruals to total assets (TATA).

Although this study found a significant national difference between countries in terms of practicing manipulation in financial statements, it did not address the issue of why some countries have higher level of manipulation and greater fluctuations in manipulation than others. Further study could be conducted to look for the reasons on these issues.

Investors and other stakeholders are advised to judge the manipulation in financial statements before fixing up for investment. At least they should examine Sales, Accounts Receivable, Depreciation, Value of Fixed Assets and Accruals data before accepting the financial statement in good faith.

The trend of manipulation in financial statements is increasing day by day and that is why it needs to prevent to protect our society from white collar crime. The cost of white collar crime is much higher and key executives are making money at the expense of investors and other stakeholders. This kind of study creates awareness among stakeholders about the manipulation as well as provides techniques to examine the faithfulness of financial statements. Then, managers will not overstate or understate either revenues or expenses easily, as it can damage the goodwill.

This is the first study of its kind addressing measurement of manipulation score, overall manipulation index (OMI) and identification of widely used variables of manipulation in financial statements are new contributions towards existing literature of earnings manipulation.

This paper aims to examine the impact of initial public offerings (IPO)-year opportunistic earnings management on long-term market and earnings performance.

A sample of 150 book-built IPOs over 2001-2006 are analysed based on industry adjusted return on sales and industry adjusted return on assets for six post-IPO years. The quality of earnings is measured in two ways using discretionary accruals and Beneish manipulation score. Modified Jones model is used to estimate the expected accruals and to compute the discretionary accruals for each IPO firm year. Regression model is used to examine the impact of IPO-year quality of earnings on future earnings performance.

The paper finds that earnings and market performance of IPO companies are abnormally higher in the IPO-year, as compared to the post-IPO years. Similarly, the quality of earnings during the IPO-year is lower than those in the post-IPO years. The results also show that the opportunistic earnings management in IPO-year has significant negative impact on the long-term adjusted earnings and market performance.

The present study is confined to the period from 2001 to 2006 for the purpose of post-IPO analysis for a period of six post-IPO years. Thus, the conclusions of this study are to be viewed with this limitation.

This paper is the first study based on the Indian context to examine the relationship between the quality of earnings of the IPO firm and long-term earnings and market performance.

This paper aims to investigate empirically the eight-variables Beneish M-model to identify occurrence of financial statement fraud or tendency to engage in earning manipulation.

A data set of 25,468 companies (Société Anonyme and Limited Liability Companies) in Greece was analyzed during two-year period of 2011-2012. Financial statements of banks are excluded.

The results showed that 8,486 companies or 33 per cent of the whole sample has a greater than −2.2 score, which is a signal that companies are likely to be manipulators. Also, for manipulators, results using F-distribution showed that days sales in receivable index (DSRI), asset quality index (AQI), depreciation index, selling, general and administrative expenses index (SGAI), total accruals to total assets index and leverage index (LVGI) are significant at 99 per cent confidence level in its effect on Beneish M-score. Also, there is a significant relationship between earning management, as expressed by Beneish M-score and each one of variables, DSRI, AQI, gross margin index, sales growth index, SGAI and LVGI. Most of all, DSRI explains 95.92 per cent of the variation in Beneish M-score in statistical terms.

Results are important for banking system, because financial statements information influence credit decisions of banks. Debt agreements include terms based upon accounting numbers. Also, using Beneish Model, it is a cheap and easy way for examiners of possible fraudulent activity.

To the best of the author’s knowledge, there is a great lack of research in Greece, using Beneish model. There is only one more study using the Beneish model, examining only a few companies listed in Athens Stock Exchange during 1999-2000. Findings have also important implications not only for banks but also for users of Greek financial statement accounts, especially to investors, auditors, regulators, to taxation and other state authorities.

The purpose of this study is to examine whether the required disclosure and the high frequency of key audit matters (KAMs) are likely to moderate the effect of higher credit risk on earnings quality.

This study uses 15,106 Taiwanese firm-year observations to explore the relationship between earnings quality and credit risk during the 2011 to 2020 period. We use the two-stage least squares method to test whether the presence of KAM disclosures moderated the association between earnings quality and credit risk and also to examine whether higher KAM frequency moderates the association between earnings quality and credit risk.

Our results provide evidence that the presence of a KAM disclosure requirement moderates the impact of firms with higher credit risk on earnings quality. In addition, there is significant evidence that the higher the frequency of KAM disclosures the greater the moderation impact that is found.

This research investigates whether the disclosure and high frequency of KAMs moderates the effect of credit riskiness on earnings quality. This study improves our understanding of whether more KAMs disclosures would improve earnings quality of firms with higher credit risk. In addition, we also use Beneish M-SCORE, as an alternative earnings quality proxy, to reinforce our empirical results. This markedly differentiates this paper from other studies.

The purpose of this paper is to examine Toshiba’s fraudulent financial reporting in relation to the fraud diamond (pressure, opportunity, rationalisation and capability).

A quantitative empirical research, analysing secondary data from Toshiba’s published annual reports before restatement, from 2008–2014 has been used. A simultaneous equations approach was used to test the hypothesis. Excel software was used to analyse secondary data and to carry out correlation analysis and descriptive statistics analysis.

This study uncovers evidence that pressure proxied by return on assets (ROA), the opportunity proxied by ineffective monitoring (BDOUT), rationalisation proxied by audit opinion (AO) and capability proxied by board member changes (BCHANGE) had moderate to strong relationship to financial statement fraud (FSF) (proxied by Beneish M-score model). However, ROA has a negative and significant effect on Toshiba’s FSF. BDOUT, AO and BCHANGE have positive and significant effect on Toshiba’s FSF. Furthermore, there is no multicollinearity problem within the four variables. Overall, this study has statistically proven that all dimensions of fraud diamond are required for the explanation of Toshiba’s accounting scandal.

Although a few studies discuss the four dimensions (fraud diamond), none, to our surprise, exists which explain the circumstances led Toshiba’s high-level executives to commit fraud. This study is the first thorough investigation of Toshiba’s accounting scandal that uses all four dimensions to explain Toshiba’s FSF.