Identifying financial statement fraud activities is very important for the sustainable development of a socio-economy, especially in China’s emerging capital market. Although many scholars have paid attention to fraud detection in recent years, they have rarely focused on both financial and non-financial predictors by using a multi-analytic approach. The present study detected financial statement fraud activities based on 17 financial and 7 non-financial variables by using six data mining techniques including support vector machine (SVM), classification and regression tree (CART), back propagation neural network (BP-NN), logistic regression (LR), Bayes classifier (Bayes) and K-nearest neighbor (KNN). Specifically, the research period was from 2008 to 2017 and the sample is companies listed on the Shanghai stock exchange and Shenzhen stock exchange, with a total of 536 companies of which 134 companies were allegedly involved in fraud. The stepwise regression and principal component analysis (PCA) were also adopted for reducing variable dimensionality. The experimental results show that the SVM data mining technique has the highest accuracy across all conditions, and after using stepwise regression, 13 significant variables were screened and the classification accuracy of almost all data mining techniques was improved. However, the first 16 principal components transformed by PCA did not yield better classification results. Therefore, the combination of SVM and the stepwise regression dimensionality reduction method was found to be a good model for detecting fraudulent financial statements.
The sustainable development of mobile government social media depends citizens’ continued use. Based on the Stimulus-Organism-Response framework and social response theory, the present study investigated the impacts of perceived similarity and anthropomorphic cues on citizens’ mobile government microblog continuance. A research model of mobile government microblog continuance was developed and empirical tested by using dataset collected from 428 mobile government microblog citizens in China. The results of structural equation modeling demonstrated that perceived similarity (including external similarity and internal similarity), and anthropomorphic cues (including social interaction value, visual appearance, and identity attractiveness), have positive influences on both cognitive and affective involvement, which further determinate mobile government microblog continuance. Considering the path coefficient and significant levels, the impact from affective involvement on mobile government microblog continuance is stronger that from cognitive involvement.
Financial fraud misleads investors into making wrong decisions based on incorrect information, especially for listed companies’ financial statements fraud. It damages investors’ interests, disturbs the economic order, and creates a crisis of trust, which is extremely harmful. Therefore, it is of great significance to build an effective financial fraud detection model for listed companies. This study uses a sample of 126 Chinese listed companies from 2013 to 2017 to examine the relationship between two organizational impression management strategies (promotion strategy and defense strategy) and financial fraud using the integrated learning methods. This study innovatively analyzes financial fraud using social media data and annual reports’ readability data as non-financial features. The results show that companies implementing the defensive/protective strategy were more likely to commit financial fraud. In addition, the average number of tables in a company’s annual report can significantly help researchers to judge fraud.
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