In order to help enterprises to achieve high-quality development and improve the capital market regulatory policies by supporting with more factual basis from China, this paper conducts research on clarifying impact mechanism of digital transformation on M&A efficiency of listed companies. Taking the mergers and acquisitions of listed companies from 2007 to 2021 as a research sample, the influence mechanism of the digital transformation degree of companies on their M&A efficiency was studied. The research results show that the digital transformation of listed companies will improve their M&A efficiency. Digital transformation will reduce the degree of mispricing stocks of M&A companies, curb conflicts between managers and agents of M&A companies, and improve their M&A efficiency. Further research finds that the promotion effect of digital transformation on M&A efficiency is more significant in non-state-owned companies, with a higher degree of financing constraint and high analyst attention. In the future, regulatory authorities should actively promote the digital transformation of listed companies, curb mispricing and management agency problems in the capital market with digital governance, and improve the efficiency of mergers and acquisitions in the capital market. This paper not only provides a more factual basis on concrete case from China but also enriches the related empirical analysis on corporate digital transformation and M&A efficiency.

In a time varying difference-in-differences setting, we empirically test the impact of high-speed rail (HSR) connections on urban clusters in China. We construct an urban cluster centrality index to reflect changes in the economic positions of counties in clusters. The results show the following: (1) The HSR connection has a positive effect on urban cluster centrality, especially for counties with higher centrality. (2) The heterogeneity effect indicates that western counties centrality has greatly increased their urban cluster centrality compared to eastern areas. (3) Mechanism estimation indicates that the different result to western and eastern counties is due to the high skill labour transfer from eastern to western counties.

In recent years, supply chain risks and stability have become a focal point of public attention. However, there is no consensus on how exogenous shocks affect the sustainability of supply chain relationships, nor a clear mechanism of influence. This study uses data from all A-share listed companies in China from Q2 2018 to Q4 2021, constructing a “supplier–quarter–customer” relationship dataset, with the COVID-19 pandemic serving as an exogenous shock. The results show that after experiencing exogenous shocks, the sustainability of supply chain relationships actually strengthens. This suggests that companies may take measures to enhance supply chain stability and maintain existing relationships to ensure sustainability. Channel analysis reveal that trade credit serves as a channel for the impact of exogenous shocks on the sustainability of supply chain relationships, with companies adjusting trade credit supply to downstream customers to maintain and strengthen stability. Additionally, the impact of exogenous shocks on the sustainability of supply chain relationships varies with market concentration, product input heterogeneity, and firms’ ownership type. Therefore, companies should enhance supply chain relationship management, utilize trade credit as a risk buffer, and optimize the supply chain structure to reduce risk transmission and maintain sustainability.

This paper proposes two data-driven models (including LSTM pricing model, WGAN pricing model) and an improved model of LSM based on GAN to analyze the pricing of convertible bonds. In addition, the LSM model with higher precision in traditional pricing model is selected for comparative study with other pricing models. It is found that the traditional LSM pricing model has a large error in the first-day pricing, and the pricing function needs to be further improved. Among the four pricing models, LSTM pricing model and WGAN pricing model have the best pricing effect. The WGAN pricing model is better than the LSTM pricing model (0.21%), and the LSM improved model (1.17%) is better than the traditional LSM model (2.26%). Applying the generative deep learning model GAN to the pricing of convertible bonds can circumvent the harsh preconditions of assumptions, and significantly improve the pricing effect of the traditional model. The scope of application of each model is different. Therefore, this paper proves the feasibility of the GAN model applied to the pricing of convertible bonds, and enriches the pricing function of derivatives in the financial field.

Let $\mathcal{A}$ be a connected cochain DG algebra such that its underlying graded algebra ${\mathcal{A}}^{\#}$ is the graded skew polynomial algebra $k⟨x_1,x_2,x_3⟩/\left(\begin{array}{c}{x}_1{x}_2+x_2{x}_1\\ x_2{x}_3+x_3{x}_2\\ x_3{x}_1+x_1{x}_3\end{array}\right),|x_1|=|x_2|=|x_3|=1.$ Then the differential ${\partial }_{\mathcal{A}}$ is determined by $\left(\begin{array}{c}{\partial }_{\mathcal{A}}\left(x_1\right)\\ {\partial }_{\mathcal{A}}\left(x_2\right)\\ {\partial }_{\mathcal{A}}\left(x_3\right)\end{array}\right)=M\left(\begin{array}{c}{x}_1^2\\ x_2^2\\ x_3^2\end{array}\right)$ for some $M\in M_3\left(k\right)$. When the rank $r\left(M\right)$ of M belongs to $\{1,2,3\}$, we compute $H\left(\mathcal{A}\right)$ case by case. The computational results in this paper give substantial support for the research of the various homological properties of such DG algebras. We find some examples, which indicate that the cohomology graded algebras of such kind of DG algebras may be not left (right) Gorenstein.

This paper constructs and optimizes, for the first time, the decomposition framework of inter-city outflow and export from the urban scale and analyzes the basic characteristics of China’s urban manufacturing industry’s participation in the global value chain and national value chain from the perspective of temporal and spatial evolution. The results show that the participation index of the manufacturing industry in coastal cities is higher than that in inland cities, and inland cities are more inclined to rely on the domestic value chain to obtain superior resources for the development of the manufacturing industry. The indicators of the manufacturing industry in the cities of the province show excellent performance, while the indicators of the peripheral cities show poor characteristics. At the same time, the length index of the global value chain of the coastal city manufacturing industry is also consistent with the actual development of the urban manufacturing industry in China. The performances of China’s urban manufacturing industry in participating in the global value chain and the domestic value chain are different. Under the background of the new development pattern, the effective docking of the double chains should be realized to achieve high-quality development in the manufacturing industry.