In order to reduce the risk of enterprise management, the financial risk early warning methods of listed companies are mainly studied. The financial risk characteristics of listed companies are analysed. With the help of rough set theory, the financial risk indicators are selected, and the financial risk early warning index system is established. The financial risk early warning model is constructed by using back propagation neural network (BPNN) algorithm based on deep learning. Finally, the accuracy and feasibility of the constructed neural network model are verified. The results show that rough set theory can be used to screen financial risk indicators and select important indicators, which can simplify the data and reduce the complexity of calculation. BPNN can calculate the simplified data and identify and evaluate the financial risk. Empirical analysis shows that the proposed method can shorten the training time of the model to a certain extent, and improve the accuracy of financial risk prediction.
In response to the rising power of electronic word of mouth (eWOM), marketers are gradually placing importance on the effects of their marketing strategies. The main objective of the study is to use both secondary as well as primary data to investigate the relationship and interaction between eWOM and online pricing. This study took BizRate UK, a price comparison website, as the database to investigate 100 mobile phone and tablet products and conducted interviews with online shoppers and managers from online retailers. After applying the test from quantitative methodologies, the result indicated the positive and negative reviews from online shoppers had an effect on pricing performance. Two types of reviews can be sorted out in a specific way as well. Finally, because the subject covers both quantitative (pricing) and qualitative (eWOM) aspects, future studies that are interested in this area should still apply the two methodologies for more accurate investigation.
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