The financial penetration accelerated by economic globalization and financial liberalization has inevitably induced market co-movement and the rising likelihood of cross-market risk contagion. An in-depth analysis concerning the carrier of risk contagion, i.e., market connectedness network, is of great significance for risk management. This study aims to establish a holistic framework to shed light on the topological dynamics and the evolving channels of connectedness network among 24 major stock markets in two aspects; namely, a dynamic perspective juxtaposing crisis and non-crisis periods, and a contrasting perspective between risk absorption and risk spillover. To this end, a methodological framework of the generalized variance decomposition and generalized exponential random graph models (GERGM) is constructed, in which the former method formulates the asymmetric causal relationships of stock return volatility among countries and regions into weighted and directed networks, and the latter method simulates and models the varying attributes of different contagion channels in the formation of tie directions and weights. The results indicate that the global stock market network reflects typical event-driven and time-varying characteristics. Countries and regions that rely heavily on foreign direct investment (FDI) are more likely to absorb risks, especially during the post-crisis recovery period, while countries and regions with higher foreign portfolio holdings are more inclined to risk spillover, especially during the subprime crisis. Geographical proximity and bilateral trade volume amplify risk contagion, whereas foreign exchange reserve holding improves robustness. This holistic framework allows the identification of the direction and intensity of risk contagion and the clarification of priority of risk transmission channels in different stages, thus reducing the uncertainty of risk management and providing insights into the macro-prudential managements toward sustainable economic development.

With the development of quantitative finance, machine learning methods used in the financial fields have been given significant attention among researchers, investors, and traders. However, in the field of stock index spot–futures arbitrage, relevant work is still rare. Furthermore, existing work is mostly retrospective, rather than anticipatory of arbitrage opportunities. To close the gap, this study uses machine learning approaches based on historical high-frequency data to forecast spot–futures arbitrage opportunities for the China Security Index (CSI) 300. Firstly, the possibility of spot–futures arbitrage opportunities is identified through econometric models. Then, Exchange-Traded-Fund (ETF)-based portfolios are built to fit the movements of CSI 300 with the least tracking errors. A strategy consisting of non-arbitrage intervals and unwinding timing indicators is derived and proven profitable in a back-test. In forecasting, four machine learning methods are adopted to predict the indicator we acquired, namely Least Absolute Shrinkage and Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Long Short-Term Memory neural network (LSTM). The performance of each algorithm is compared from two perspectives. One is an error perspective based on the Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and goodness of fit (R²). Another is a return perspective based on the trade yield and the number of arbitrage opportunities captured. Finally, a performance heterogeneity analysis is conducted based on the separation of bull and bear markets. The results show that LSTM outperforms all other algorithms over the entire time period, with an RMSE of 0.00813, MAPE of 0.70 percent, R² of 92.09 percent, and an arbitrage return of 58.18 percent. Meanwhile, in certain market conditions, namely both the bull market and bear market separately with a shorter period, LASSO can outperform.

Information transmission of innovation and peer effect among firms have significant impacts on innovation adoption decisions of firms, but those effects usually depend on the different types of interfirm networks. Therefore, the diffusion of innovation (DOI) can be regarded as a process occurring in complex systems composed of different types of interactions, which can be categorized into links belonging to different layers of multiplex networks. This study aims to shed light on the complexity of innovation diffusion from the perspective of an information-behavior framework based on multiplex networks. The process of technology innovation adoption is formulated into two stages including information perception and decision making, and a novel innovation diffusion model based on duplex inter-firm networks, in which one layer has influence on information transmission and the other carrys the peer effect, is further proposed in this study. The simulation experiments indicate that the random-scale-free duplex networks are favourable to the diffusion speed while scale-free-scale-free duplex networks are conducive to the diffusion range. Moreover, within the scale-free-scale-free duplex networks, the diffusion speed will increase with the increasing of the power law index in the information network and decrease with the increasing of the power law index of the behavior network. The study contributes to the literature by establishing duplex network model that distinguishes links between information and behavior networks, and offers insights concerning optimization of network configuration in the promotion of DOI.

With the growing emphasis on ESG (Environmental, Social, and Governance) issues, the mandatory disclosure of ESG reports is on the horizon. However, due to the lack of a regulatory framework and a unified international ESG evaluation, the phenomenon of greenwashing in corporate ESG reporting is prevalent. We collected social responsibility reports and actual ESG performance data from A-share companies from 2011 to 2021 and innovatively employed text mining techniques to quantitatively investigate the extent of greenwashing in ESG reports. Our study initially utilized the Word2Vec method, combined with Skip-gram and Continuous Bag of Words models to train word vectors, and built an ESG lexicon using seed words. ESG reports is subsequently segmented based on a defined sentence splitting function and TF-IDF algorithm is employed to extract keywords. By matching the keywords with the ESG lexicon, we precisely extracted the annual ESG discourse for each company and conducted sentiment analysis to derive a greenwashing score. Heterogeneity analysis reveals that firm ownership has no significant impact on the level of greenwashing, yet the industry and region in which the enterprise operates considerably influence the greenwashing level. This study holds implications for enhancing the quality of ESG reporting and optimizing investment decisions.