The study of interconnections between various sectors of the national economy is crucial for understanding the pattern and pace of macroeconomic growth. This paper analyzes the macroeconomic impact of shocks occurring in specific sectors through both supply and demand perspectives and proposes a combination of bottom-up and top-down structural path analysis approaches to trace the transmission path of network spillover effects, where shocks in this paper refer to microeconomic productivity changes and network spillover is defined as the effect on GDP due to the propagation of shocks to other sectors. The research results found that the total spillover effect of primary and secondary industry sectors in China shows an inverted U-shape, and the total spillover effect of tertiary industry sectors shows an upward trend. A large total spillover effect of a sector does not mean that both upward and downward spillover effects are large; for example, the construction industry has high upward spillover effects and low downward spillover effects. The spillover effect of each production layer decreases as the path lengthens, and the distribution is Lshaped.In addition, by identifying the critical paths of spillover effects, we find that the spillover effects of labor-intensive industries, such as wholesale and retail, are decreasing year by year, and the spillover effects of the paths related to the information technology industry are gradually occupying an important position.

The COVID-19 pandemic has inflicted substantial impacts on global financial markets and the economy. This study explores the impact of two pandemic outbreaks in China on its stock market industries. It employs the Conditional Autoregressive Value at Risk (CAViaR) model to compute tail risks across 16 selected industry sectors. Additionally, risk correlation networks are constructed to illustrate the risk correlations among industry sectors during different phases of the two outbreaks. Furthermore, risk contagion networks are built based on the Granger causality test to examine the similarities and differences in the contagion mechanisms between the two outbreaks. The findings of this study show that (i) the two outbreaks of COVID-19 have resulted in tail risks for most industries in the Chinese stock market. (ii) The risk correlation network became more compact because of both outbreaks. The impact of the second outbreak on the network was less severe than that of the first outbreak. (iii) During the first outbreak of COVID-19, the financial industry was the primary source of risk output; during the second outbreak, the concentrated outbreak in Shanghai led the industries closely related to the city's economy and trade to become the most significant risk industries. These findings have practical implications for researchers and decision-makers in terms of risk contagion among stock market industries under major public emergencies.

In this paper, we examine the prediction performance using a principal component analysis (PCA). In particular, we perform a PCA to identify significant factors (principal components) and then use these factors to form predictions of stock price movements. We apply this strategy on the Chinese stock markets. Using data from January 2, 2019 till September 16, 2021, the empirical results show substantial out-performances from the PCA-based predictions against a naïve buy-and-hold strategy and also single time-series predictions of individual stocks. Next we examine if the factors retrieved from PCA are indeed important contributing factors in explaining stock price movements. To do this, we adopt a machine learning technique popular in studying stock performances – random forest. We discover that, comparing to widely used descriptive factors such as industry sector, geographical location, and market types (known as “board” or “ban” in Mandarin), principal components rank very highly among those descriptive factors.