Financial Statements Frauds: Does Their Intensity Have a Connection with Earnings Management?

This study investigates the relationship between earnings management and financial statements frauds.We examine how earnings management practices;done in the two years before the fraud,impact the likelihood of fraud occurrence.Moreover,we introduce a new measure for the fraud intensity.Using a sample of 70 fraud and 70 no-fraud firms,we find that firms committing fraud of higher intensity have managed earnings in the two years before the fraud occurrence.This paper contributes to the literature about fraud antecedents because it is the first study measuring the relationship between earnings management and the intensity of the fraud,and it can be also useful for practitioners,because using the analysis of earnings management practices,analysts can foresee and prevent financial statements frauds.

Study on how to elevate the discriminating ability of the discriminating model of the financial statement fraud

By summarizing the factor of the financial statement fraud in existing research outcome, the paper confirms the discriminating characteristic of the financial statement fraud and sets up a theoretic model to discriminate the financial statement fraud. Using radial basis function neural network, regarding to the swatch that the listed company that is punished by the Securities Regulatory Commission or the Ministry of Finance, and according to the clustering elements, validating across by set one aside, the paper verifies respectively the 22 characteristics and 31 characteristics of discriminating model. According to the clustering elements, validating across by set one aside, the paper verifies respectively the 31 characteristics and 8 characteristics selected by Fisher-ratio of discriminating model. The research outcome indicates the discriminating ability of the model including 8 characteristics is better elevated than the traditional model including 31 characteristics by comparing the disciplinary error and the forecast precision.

Positive Research on the Financial Statement Fraud Factors of Listed Companies in China

This paper studied the ＂pressure＂ and ＂opportunity＂ factors that caused financial statement fraud on the basis of the data of 41 A-share listed companies （1999-2004） in China which had been forfeited by China Securities Regulatory Commission （SRC） because of accounting irregularities. The author found that avoiding ＂ST＂ and ＂PT＂ was the primary pressure, and the opportunities mainly came from the higher top 10 shareholders＇ ownership concentration, the lower proportion of independent directors, the fewer number of directorate meetings and shares owned by the directorate members, board chairman and CEO held by one person and the ineffective supervisor boards. We also found that the companies involved financial statement fraud had the lower first majority shareholder＇s share proportion and they changed CPA firm more frequently.

Principal Model Analysis Based on Bagging PLS and PCA and Its Application in Financial Statement Fraud

Motivated by the Bagging Partial Least Squares(Bagging PLS)and Principal Component Analysis(PCA)algorithms,a novel approach known as Principal Model Analysis(PMA)method is introduced in this paper.In the proposed PMA algorithm,the PCA and the Bagging PLS are combined.In this method,multiple PLS models are trained on sub-training sets,derived from the training set using the random sampling with replacement approach.The regression coefficients of all the sub-PLS models are fused in a joint regression coefficient matrix.The final projection direction is then estimated by performing the PCA on the joint regression coefficient matrix.Subsequently,the proposed PMA method is compared with other traditional dimension reduction methods,such as PLS,Bagging PLS,Linear discriminant analysis(LDA)and PLS-LDA.Experimental results on six public datasets demonstrate that our proposed method consistently outperforms other approaches in terms of classification performance and exhibits greater stability.Additionally,it is employed in the application of financial statement fraud identification.PMA and other five algorithms are utilized to financial statement fraud which concerned by the academic community,and the results indicate that the classification of PMA surpassed that of the other methods.