Yongzeng Lai

The occurrence of systemic financial risk is a process of systemic structural reconstruction of the financial system in response to a shock. From a complex network perspective, the characteristics of the network nodes and the links between them determine the extent of financial risk accumulation and the size of the risk released when the structure of the financial system is rebuilt. This paper develops a complex network model of the banking system containing direct shocks, interbank market contagion, and common asset contagion of banking institutions with two insolvency scenarios of illiquidity and insolvency. The relationship between the network node characteristics of the average leverage of the banking institutions, the characteristics of the edges of the network of the average connectivity strength of the interbank market, the average connectivity strength of the banks’ external assets, and the accumulation of risk in the financial system are investigated. Our study finds the following: (1) The higher the average leverage, the higher the risk accumulation. (2) The higher the average connection strength in the interbank market, the higher the degree of risk accumulation. (3) The stronger the average connectivity strength of a banking institution’s external assets, the higher the risk accumulation. (4) When the level of risk accumulation in the financial system is low, the system may absorb external risks, leading to an increase in risk accumulation. (5) When the level of risk accumulation is high, management can adjust regulatory policies to release accumulated risks in an orderly manner, thereby reducing the level of risk accumulation in the financial system.

To ensure the security and confidentiality of various data types (including text, images, audio, and video), this paper proposes a multi-wavelet figure-and-text hiding algorithm (MWFTHA) and its corresponding multi-wavelet figure-and-text restoration algorithm (MWFTRA). These algorithms facilitate the encoding and embedding of text and color images into a one-dimensional signal through multi-wavelet transforms. Text data is encoded using a character dataset, while color images are processed via a linear transformation before being integrated into the signal. Subsequently, the original text and image can be precisely retrieved from the synthesized signal using MWFTRA. An illustrative case demonstrates the efficacy of this approach. The efficiency of MWFTHA and MWFTRA is verified through 1,000 simulations. The results indicate rapid data hiding and recovery, as indicated by the mean execution time and standard deviation. The method's performance is evaluated using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR), which indicate slight improvements in quality relative to traditional wavelet and integer wavelet transforms. Additionally, the system's security is analyzed, with a focus on private-key mechanisms and resistance to data tampering. This steganography technology provides a robust solution for the secure transmission and storage of sensitive data, thereby reducing the risk of information leakage.

Crude oil is a raw and natural, but nonrenewable, resource. It is one of the world’s most important commodities, and its price can have ripple effects throughout the broader economy. Accurately predicting crude oil prices is vital for investment decisions but it remains challenging. Due to the deficiencies neglecting residual factors when forecasting using conventional combination models, such as the autoregressive moving average and the long short-term memory for prediction, the variational mode decomposition (VMD)-empirical mode decomposition (EMD)-Transformer model is proposed to predict crude oil prices in this study. This model integrates a second decomposition and Transformer model-based machine learning method. More specifically, we employ the VMD technique to decompose the original sequence into variational mode filtering (VMF) and a residual sequence, followed by using EMD to decompose the residual sequence. Ultimately, we apply the Transformer model to predict the decomposed modal components and superimpose the results to produce the final forecasted prices. Further empirical test results demonstrate that the proposed quadratic decomposition composite model can comprehensively identify the characteristics of WTI and Brent crude oil futures daily price series. The test results illustrate that the proposed VMD–EMD–Transformer model outperforms the other three models—long short-term memory (LSTM), Transformer, and VMD–Transformer in forecasting crude oil prices. Details are presented in the empirical study part.

The drastic fluctuations in pork prices directly affect the sustainable development of pig farming, agriculture, and feed processing industries, reducing people’s happiness and sense of gain. Although there have been extensive studies on pork price prediction and early warning in the literature, some problems still need further study. Based on the monthly time series data of pork prices and other 11 influencing prices (variables) such as beef, hog, piglet, etc., in China from January 2000 to November 2023, we have established a project pursuit auto-regression (PPAR) and a hybrid PPAR (H-PPAR) model. The results of the PPAR model study show that the monthly pork prices in the lagged periods one to three have an important impact on the current monthly pork price. The first lagged period has the largest and most positive impact. The second lagged period has the second and a negative impact. We built the H-PPAR model using the 11 independent variables (prices), including the prices of corn, hog, mutton, hen’s egg, and beef in lagged period one, the piglet’s price in lagged period six, and by deleting non-important variables. The results of the H-PPAR model show that the hog price in lagged period one is the most critical factor, and beef price and the other six influencing variables are essential factors. The model’s performance metrics show that the PPAR and H-PPAR models outperform approaches such as support vector regression, error backpropagation neural network, dynamic model average, etc., and possess better suitability, applicability, and reliability. Our results forecast the changing trend of the monthly pork price and provide policy insights for administrators and pig farmers to control and adjust the monthly pork price and further enhance the health and sustainable development of the hog farming industry.

According to the United Nations World Tourism Organization, tourism promotes sustainable economic development. Ensuring tourism safety is an essential prerequisite for its sustainable development. In this paper, based on the three evaluation index systems for tourism safety early warning and the collected sample data, we establish three projection pursuit dynamic cluster (PPDC) models by applying group search optimization, a type of swarm intelligence algorithm. Based on case studies, it is confirmed that the results derived from the PPDC models are consistent with the expert judgments. The importance of the evaluation indicators can be sorted and classified according to the obtained optimal projection pursuit vector coefficients, and the tourism risks of the destinations can be ranked according to the sample projection values. Among the three aspects influencing tourism safety in case one, the stability of the tourism destination has the most significant impact, followed by the frequency of disasters. Of the ten evaluation indicators, the frequency of epidemic disease affects tourism safety the most, and the unemployment ratio affects it the second most. Overall, the PPDC model can be adopted for tourism safety early warning with high-dimensional non-linear and non-normal distribution data modeling, as it overcomes the “curse of dimensionality” and the limitations associated with small sample sizes.

Accurate stock price prediction has an important role in stock investment. Because stock price data are characterized by high frequency, nonlinearity, and long memory, predicting stock prices precisely is challenging. Various forecasting methods have been proposed, from classical time series methods to machine-learning-based methods, such as random forest (RF), recurrent neural network (RNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM) neural networks and their variants, etc. Each method can reach a certain level of accuracy but also has its limitations. In this paper, a CNN-BiLSTM-Attention-based model is proposed to boost the accuracy of predicting stock prices and indices. First, the temporal features of sequence data are extracted using a convolutional neural network (CNN) and bi-directional long and short-term memory (BiLSTM) network. Then, an attention mechanism is introduced to fit weight assignments to the information features automatically; and finally, the final prediction results are output through the dense layer. The proposed method was first used to predict the price of the Chinese stock index—the CSI300 index and was found to be more accurate than any of the other three methods—LSTM, CNN-LSTM, CNN-LSTM-Attention. In order to investigate whether the proposed model is robustly effective in predicting stock indices, three other stock indices in China and eight international stock indices were selected to test, and the robust effectiveness of the CNN-BiLSTM-Attention model in predicting stock prices was confirmed. Comparing this method with the LSTM, CNN-LSTM, and CNN-LSTM-Attention models, it is found that the accuracy of stock price prediction is highest using the CNN-BiLSTM-Attention model in almost all cases.