Firstly, the article elaborates on the concepts of metaverse and industrial metaverse, pointing out that the metaverse has driven changes and optimizations in multiple dimensions such as urban form, social organization form, and industrial production form; Secondly, the metaverse has empowered urban governance in Guangzhou, improving the efficiency of urban management, enhancing the city's emergency management capabilities, improving the quality of interaction between people and the city, and promoting the construction of a smart city; Once again, the focus was on the practices and good results achieved by Guangzhou in utilizing blockchain technology, digital twin technology, generative artificial intelligence technology, unmanned aerial vehicles+AI and other technologies in urban governance and serving the public; Finally, it is clarified that metaverse related technologies will promote the integration of carbon based civilization and silicon-based civilization in urban and social governance. Humans can use silicon-based civilization technology to expand their living space and improve their quality of life, while silicon-based civilization can also draw inspiration from the culture and emotions of carbon based life, achieving more comprehensive development.

This paper, based on data from Chinese A-share listed companies between 2009 and 2019, employs the implementation of the "Shanghai-Hong Kong Stock Connect" as a landmark event of capital market liberalization, utilizing a difference-in-differences model to empirically examine the impact of market openness on firms' cross-region investment behavior and its underlying mechanisms. The findings indicate that: (1) the launch of the "Shanghai-Hong Kong Stock Connect" has significantly promoted the establishment of cross-provincial and cross-border subsidiaries by the companies involved; (2) capital market liberalization influences firms' cross-region investment through three dimensions: finance, governance, and stakeholders. In terms of finance, the openness alleviated financing constraints and improved stock liquidity; in governance, it pressured companies to adopt more digitalized and transparent governance structures to accommodate cross-regional expansion; in the stakeholder dimension, it attracted the attention of external investors, accelerating their understanding of firms and alleviating the trust issues associated with cross-region expansion. (3) The effect of capital market liberalization on promoting cross-border investments by private enterprises is particularly pronounced, and this effect is further strengthened as the quality of corporate information disclosure improves. Firms with higher levels of product diversification benefit more from market liberalization, accelerating their overseas expansion. (4) Capital market liberalization has elevated the level of cross-region investment, thereby significantly fostering innovation and improving investment efficiency. The conclusions of this study provide fresh empirical evidence for understanding the microeconomic effects of China's capital market liberalization, the intrinsic mechanisms of corporate cross-region investments, and their economic consequences.

This paper models the effect of corporate information preference on stock return volatility based on optimization problems of information decisions for firms and investors. Our model hypothesizes a positive correlation between corporate information preference and volatility. Utilizing the ideal institutional background of the Chinese stock market, we empirically confirm that corporate information preference has a positive impact on volatility, particularly for firms facing more severe financial distress, limited investor attention, and fewer analyst coverage. Our study provides a new perspective for analyzing the interaction between information supply and asset price dynamics.

Taking China Pilot Free Trade Zone (PFTZ) as a new perspective of institutional environment optimization, this paper investigates its impact on corporate ESG performance. We find that the PFTZ positively enhances corporate ESG performance, which remains robust after various checks. The mechanism analysis shows that improving corporate environmental protection capacity and management efficiency are the main channels while strengthening labor protection and easing financial constraints can enhance the positive effect. Moreover, the positive effect of the PFTZ on corporate ESG performance is more pronounced in coastal regions, the service sector, and state-owned enterprises (SOEs).

As a vital documentation for assessments, rewards and punishments in terms of political promotions, the intellectual property pilot city policy (IPPC), an strategic incentive measure to enhance innovation capacities at the city and firm level, may play a prominent role in innovation fostering in China. Yet patent bubbles that focus more on quantity over quality have been thrown into doubt, as local cadres and firms shall pay more attention to the easier observable low-quality innovation performance amid the pressure of political task. this paper conducts an investigation that drew upon listed firm data from 270 prefecture-level cities and employs a PSM-DID design to evaluate the IPPC policy effectiveness on innovation quality and innovation quantity. The results are obvious: The policy have boosted the number of innovations, but has a limited effect on improving the quality of innovation. We further apply a hierarchical liner modeling approach to deal with the stratified cityand firm-level data and to verify the mechanism through which policy distortions may affect corporate innovation. There also gives evidence that the IPPC policy comes into effect mainly through financial subsidies, institutional supply and the intensity of IPR protection at the local scale. This report concludes by proposing further policy implementations for the future optimization of China’s innovation strategies.

The objective of this study is to create optimal two-asset portfolios consisting of stocks from Western Europe, the United States, and the BRICS (Brazil, China, India, Russia, and South Africa), as well as sixteen commodity types during the BREXIT period. We utilized dynamic variances and covariances from the GARCH model to derive weights for the two-asset portfolios, with each portfolio consisting of one equity factor and one commodity factor. Subsequently, hedge ratios were calculated for these various assets. Our findings indicate that portfolios consisting of European stocks do not require the inclusion of commodities, whereas the other equities do.

Volatility in the financial markets is commonplace and it comes with a cost. One of these costs is abrupt and huge drop in stock price that is known as stock price crash. To model this, we propose a new machine-learning based stock crash risk measure using minimum covariance determinant (MCD) to detect stock price crash. Using this proposed dependent variable, we try to predict stock price crash using cross-sectional regression. The findings confirm that the method properly capture the stock price crash and our proposed model performs well in terms of statistical significance and financial impact. Moreover, using newly introduced firm-specific investor sentiment index, it is identified that stock price crash and firm-specific investor sentiment are positively correlated. That is, higher sentiment leads to an increase with stock price crash risk, a relation that remains robust even when different firm sizes and detoned firm-specific investor sentiment index are considered.

This paper proposes a new forecasting framework for the stock market that combines machine learning algorithms with several technical analyses. The paper considers three different algorithms: the Random Forests (RF), the Gradient-boosted Trees (GBT), and the Deep Neural Networks (DNN), and performs forecasting tasks and statistical arbitrage strategies. The portfolio weight optimization strategy is also proposed to capture the model's return and risk information from output probabilities. The paper then uses the stock data in the Chinese A-share market from January 1, 2011, to December 31, 2020, and observes that all three machine learning models achieve significant returns in the Chinese stock market. The DNN achieves an average daily return of 0.78% before transaction costs, outperforming the 0.58% of the RF and 0.48% of the GBT, far exceeding the general market level. The performance of the weighted portfolio based on the ESG score is also improved in all three machine learning strategies compared to the equally weighted portfolio. These results help bridge the gap between academic research and professional investments and offer practical implications for financial asset pricing modelling and corporate investment decisions.

China commodity futures market network is constructed. Commodity is the node of the network, and the network link is defined by the price correlation matrix. We analyze the relationship between the centrality of each commodity in the commodity futures market network and the optimal weight of the commodity portfolio, empirically examine the market system factors and commodity personalized factors that affect the centrality of commodity, and evaluate the effect of network structure on the optimization of commodity portfolio selection under the mean-variance framework. It is found that the commodities with high network centrality are often related to industrial products and have high volatility. Commodities with higher centrality have lower portfolio weights. We put forward a kind of commodity futures investment strategy based on network, according to the network centricity grouping the commodities, the network centricity lower edge of the commodity structure of the portfolio, cumulative yield is better than that of centricity higher core product portfolio, the whole market portfolio yield, but due to large maximum retracement, lead to the stability and ability to resist risk compared with the other two groups of goods combination. The main contribution of this paper is to optimize portfolio selection by establishing the relationship between portfolio weight and commodity centrality by using commodity futures market network as a tool.

We examine effects of capital structure management and misvaluation on the payment method in mergers and acquisitions. In a sample of 3,097 transactions, we find evidence both for leverage optimization and misvaluation as drivers for the decision to pay with cash or stock. Our evidence also shows that it is difficult to pay with overvalued stock unless justified by economic fundamentals. Few bidders try and often only succeed after going hostile. Paying with cash while capital structure optimization suggests stock payment is more common. These firms are reluctant to pay with undervalued stock and experience positive long-term excess returns.