This paper analyzes the dynamic time-frequency volatility spillovers among the international stock markets during the Russian-Ukraine conflict. We use the VAR-based connectedness framework to calculate the volatility spillovers. Results show that (1) the trend of the total spillover is consistent with the time of the Russian-Ukraine conflict; (2) Russian stock market is the primary source and net exporter of risk; (3) the Russian government has effectively controlled the further spread of risk through policy adjustments; and (4) Russian stock market may generate long-run volatility spillovers among the international stock market. We add research related to the impact of the Russia-Ukraine conflict on international stock markets by analyzing the results of the volatility spillovers.

In existing image recognition algorithms, the position and sequence of image pixels are key factors that affect the accuracy of image recognition. Therefore, the topological invariance of complex networks has led to the recognition that applying complex networks to image recognition analysis will significantly reduce the impact of images on classification recognition accuracy when rotation, translation, and scaling occur. However, most studies on image classification by complex networks have focused on a single network, lacking dynamic evolution with the networks among them. In this paper, we propose a new complex network classification method that combines complex networks and convolutional neural networks(CNN) to train classification using deep learning. We show that the method has high classification accuracy and distinct network features and compares well with a single complex network approach. In addition, to make the distribution of the degree histogram of the image more uniform and concentrated, the original formula for calculating the power value was optimized to reduce the influence of the radius parameter on the power value.

No abstract

No abstract

It has been widely accepted that association rules mining, the task of searching for correlations between items in a database, can discover useful rules in stock analysis. Previous studies mainly emphasize on mining intratransaction associations. In this paper, we introduce the concept of intertransaction and the FITI algorithm so that we can effectively forecast the price changes in Chinese capital markets, then we compare FITI with EH-Apriori, and demonstrate the advantages of FITI over EHApriori. At the end of this paper, we apply the algorithm to a dataset of Chinese asset indices and the results indicate the usefulness of intertransaction association rules in price prediction.