Abstract Based on the frame theory and signalling theory, this article uses financial technology (fintech) to analyse online news sentiment and proposes the relevant hypothesis of the influence of news relevance on stock market risk, and conducts an empirical study on the VAR model of the stock return rate by using the news relevance and news sentiment data of the Uqer database. The results show a two‐way Granger causality between news sentiment and stock returns. News relevance is not Granger causality of yield but Granger causality of news sentiment. In addition, combined with the impulse response, variance decomposition, and relevance analysis, it is found that the news relevance degree positively affects news sentiment and then indirectly affects the stock return rate. Our findings indicate that news relevance is an essential variable in analysing online text sentiment and stock market volatility, and online news will increase the volatility of stock market risk. These results contribute to the financialization literature and guide fintech enterprises to balance market risk by combining signalling and frame theory to improve news relevance.

The rapid development of Fintech (financial technology) urges Fintech platforms to better understand users’ perceptions and behaviors toward Fintech use. Based on the theoretical framework of trust, risk perception, platform governance, and information system continuous use theories, we construct a model to explore how users’ different trust perceptions on Fintech platforms affect their risk perception, platform governance perception, and continuance intention of using Fintech. Our findings indicate users’ institutional trust, technology trust, and interpersonal trust all impact their risk perception, perception of Fintech platform governance, and continuance intention to use Fintech in different ways and magnitudes. Furthermore, platform governance perception mediates between institutional trust, interpersonal trust, and continuance intention. Our research helps the practitioners of Fintech platforms attract and retain users in the highly competitive market and provides a reference for the governance of their platforms.

Outlier detection is currently applied in many fields, where existing research focuses on improving imbalanced data or enhancing classification accuracy. In the financial area, financial fraud detection puts higher demands on real-time and interpretability. This paper attempts to develop a credit risk model for financial fraud detection based on an extreme gradient boosting tree (XGBoost). SMOTE is adopted to deal with imbalanced data. AUC is the assessment indicator, and the running time is taken as the reference to compare with other frequently used classification algorithms. The results indicate that the method proposed by this paper performs better than others. At the same time, XGBoost can obtain a ranking of important features that impact the classification results when performing classification tasks, making the evaluation results of the model interpretable. The above shows that the model proposed in the paper is more practical in solving credit risk assessment problems. It has faster response times, reduced costs, and better interpretability.

From the perspective of the inhibition of privacy computing theory, taking user satisfaction as risk perception, this paper discusses how the readability of financial APP privacy policies inhibits user satisfaction, thus affecting enterprise performance. Firstly, this paper measures the readability of privacy policies by constructing a professional vocabulary and an improved Fog Index formula. Secondly, this paper collects 615 APP privacy policies and corresponding enterprise data and empirically analyzes the impact of the readability of financial APP privacy policies on enterprise performance. This paper enriches research in the field of financial technology and empirically finds that the readability of financial technology APP privacy policies has a negative impact on enterprise performance, while user satisfaction has a positive impact on enterprise performance. In practice, this paper provides a reference for financial technology enterprises to improve the readability of APP privacy policies to improve user satisfaction and enterprise performance.

The fast growth in online word of mouth (online WOM) reviews has witnessed their wide applications in assisting customers in their purchases. While many positive factors about these reviews have been identified, biases may result from reviewers’ self-selection behavior. To examine whether online WOM ratings reflect authentic customer purchase feelings, this study focuses on the relation between customer dissatisfaction and negative reviews. First, the reviews are fine grained into feature-opinion pairs, after which three explanatory variables are designed to represent the individual, collective, and comprehensive (individual and collective) complaints, with the online rating being considered as the explained variable. Finally, a simple linear regression model is built to determine the relation between customer dissatisfaction (represented by discounting marks of ratings) and negative reviews. Our analysis finds that personal complaints have a positive effect on rating and the most frequent customer complaints have no significant effect on customer ratings.

In order to solve the problem of cross-time and cross-regional medical collaboration and distributed knowledge sharing across patients and medical teams for 24 hours a day in the context of global resource allocation, a new 24HrKF e-health service model is proposed and a key knowledge model of 24HrKF e-health team knowledge sharing is established based on existing research results combined with 24HrKF distributed team characteristics. Finally, the questionnaire data of 338 multinational medical team members are used to verify the impact of key factors on the knowledge sharing of medical teams. The results shows that factors such as information and communication technology, the hospital's cultural characteristics, cross-cultural communication, medical knowledge and skills, and trust all have a significantly positive impact on knowledge sharing among team members especially information and communication technology and medical knowledge and skills. However, the degree of time-span separation across time zones has no significant effect on the knowledge sharing among team members.

Because of the semantic gap between low-level feature and high-level semantic feature of images, the results of the traditional color-based image retrieval canpsilat meet userspsila needs. In order to eliminate interference factors in the image retrieval, use image semantic feature and improve the accuracy of image retrieval, the paper introduces an algorithm based on the color correlation mining. It regards the pixel rows as a transaction set, uses the Apriori algorithm to find out the rows by looking for the continual co-occurrence color in the transaction set. These rows are correlative with the semantic object of the image. Then it extracts the correlative color histogram of image form the correlative color set to realize the correlative color mining.

In order to improve the level of decision making and competitive advantage, organizations try to learn and develop new knowledge management techniques that are suited for the evolving global economy. Decision makers are becoming increasingly faced with a dilemma: great difficulty arises in sharing knowledge where distributed and heterogeneous data sources are so rich in terms of their information content. In order to address the problem of knowledge sharing, this article proposes a common knowledge sharing model— the 24-hour knowledge factory—of grid computing founded on case-based reasoning (CBR). This article begins with a description of the 24-hour knowledge factory, the enterprise common knowledge shared (ECKS) methodology and the time-shift sharing model. Next, the notion of a CBR-adapted approach for 24-hour knowledge factory, based on grid computing, is presented. Third, based on the ECKS concept, this article analyzes the use of the model described in this article in decision evolution and builds upon enterprise sharing structure ECK modeling, focusing on activity-work sharing, passive-work sharing and mix-work sharing. Multiple types of enterprise common knowledge transfer mechanisms are presented in this article.