Developing an early warning model for company financial crises holds critical significance in robust risk management and ensuring the enduring stability of the capital market. Although the existing research has achieved rich results, the disadvantages of insufficient text information mining and poor model performance still exist. To alleviate the problem of insufficient text information mining, we collect related financial and annual report data from 820 listed companies in mainland China from 2018 to 2023 by using sophisticated web crawlers and advanced text sentiment analysis technologies and using missing value interpolation, standardization, and data balancing to build multi-source datasets of companies. Ranking the feature importance of multi-source data promotes understanding the formation of financial crises for companies. In the meantime, a novel Attention-based Fuzzy Neural Network (AFNN) was proposed to parse multi-source data to forecast financial crises among listed companies. Experimental results indicate that AFNN exhibits significantly improved performance compared to other advanced methods.

This article proposes a semi-martingale approximation to a fractional Lévy process that is capable of capturing long and short memory in the stochastic process together with fat tails. The authors use the semi-martingale process in option pricing and empirically compare its performance to other option pricing models, including a stochastic volatility Lévy process. They contribute to the empirical literature by being the first to report the implied Hurst index computed from observed option prices using the Lévy process model. Calibrating the implied Hurst index of S&P 500 option prices in a period that covers the 2008 financial crisis, they find that the risk-neutral measure is characterized by a short memory in turbulent markets and a long memory in calm markets.

Since the global financial crisis of 2007–2009, preventing financial crises has become one of the most important objectives of regulators and banks. Although previous studies have identified the phenomenon of risk contagion in the banking system, the underlying mechanisms of risk contagion are still unclear. This study delves into the multi-stage contagion mechanism of liquidity risk based on interbank lending linkages and clearing rules and introduces a new index to quantify bank liquidity risk. We find that the contagion of liquidity risk is primarily determined by the network structure of risk exposures between banks in default and is not significantly influenced by the lending relationships of banks that remain solvent. The empirical results suggest that banks with high risk should be prioritized for cash injections to improve system liquidity. These findings offer new insights into financial risk contagion and practical recommendations for regulatory authorities formulating intervention strategies and for banks conducting risk management.

This paper investigates the impact of two global crises, the global financial crisis and the COVID-19 crisis, on herding behavior in the Chinese and U.S. stock markets. We find no evidence of herding behavior during these two global crises in the U.S. stock market, yet significant herding emerges under the COVID-19 crisis in Chinese mainland stock market. Additionally, the observed herding behavior in mainland China is primarily driven by sentiment. Our results reveal and explain the differences in the effects of financial crisis and public health crisis on herding behavior, as well as variations between emerging and developed stock markets.

We examine the interest rate pass-through in Hong Kong (HK) and Macao to see if the monetary policy transmission mechanism has been impaired since the Global Financial Crisis (GFC). Our results show that, in the post-GFC period, both the long-run and short-run interest rate pass-through from policy rates to prime rates have disappeared in Macao and weakened significantly in HK. The long-term relationship between deposit rates and policy rates no longer exists in either market while the short-term relationship has been reduced significantly. The results indicate that the effectiveness of the monetary policy in HK and Macao has been seriously undermined after the GFC and alternative monetary policy tools were needed.

Starting from document research, this paper analyzes the mechanism of the risk spillover effect from developed capital markets to the Chinese capital market. After that, this paper conducts an empirical study on the risk spillover effect of developed capital markets on the Chinese capital market by using the DCC-GARCH model. Then the impact degree of global major stock market fluctuations on the Chinese stock market is measured. The analysis shows that there exists a significant risk spillover effect of developed capital markets on the Chinese capital market, but the effect began to weaken after the financial crisis and the size of the spillover effect can be affected by macro factors such as geographical locations, foreign trade, and foreign investment.

The finance–growth nexus has been a central question in understanding the unprecedented success of the Chinese economy. With unique data on all the registered firms in China, we build extensive ownership networks, reflecting firm-to-firm equity investment relationships, and show that thesenetworks have been expanding rapidly since the 2000s, with more than five million firms in at least one network by 2017. Entering a network and increasing network centrality, both globally and locally, are associated with higher firm growth. Such positive network effects tend to be more pronounced for high productivity and privately owned firms. The RMB 4 trillion stimulus, mostly in the form of newly issued bank loans and launched by the Chinese government in November 2008 in response to the global financial crisis, partially ‘crowded out’ the positive network effects. Our analysis suggests that equity ownership networks and bank credit tend to act as substitutes for state-owned enterprises, but as complements for privately owned firms in promoting growth.

China’s shadow banking system has experienced surprisingly high growth since the global financial crisis. We develop a model to understand this puzzling phenomenon. With local government interventions in bank loans for low-quality projects and information asymmetry between banks and regulators, a policy combination of tightening formal banking and loosening shadow banking can reduce inefficiency, because the higher funding liquidity risk of shadow banking incentivizes banks to be more disciplined about the quality of projects. We find consistent empirical evidence that when on-balance-sheet financing was constrained by regulators, banks primarily shifted high-quality projects into their controlled shadow banking system.

Climate change has become mankind’s main challenge. Greenhouse gas (GHG) emissions from shipping are not irresponsible for this, representing 3% of the global total; an amount equal to that of Germany’s emissions. The Fourth Greenhouse Gas Study 2020 of the International Maritime Organization (IMO) predicts that the proportion of GHG emissions from shipping will rise further, as global trade continues to recover and grow, along with the economic development of India, China and Africa. China and the European Union have proposed to include shipping in their carbon emissions trading systems (ETS). As a result, the study of the relationship between the carbon finance market and the shipping industry, attempted here for the first time, is particularly important both for policymakers and shipowners. We use wavelet analysis and the spillover index methods to explore the dynamic dependence and information spillovers between the carbon finance market and shipping. We discover a long-term dependence and information linkages between the two markets, with the carbon finance market being the dominant one. Major events, such as the 2009 global financial crisis; Brexit in 2016; the 2018 China-US trade frictions; and COVID-19 are shown to strengthen the dependence of carbon finance and shipping. We find that the dependence is strongest between the EU carbon finance market and dry bulk shipping, while the link is weaker in the case of tanker shipping. Nonetheless, carbon finance and tanker shipping showed a relatively stronger dependence when OPEC refused to cut production in 2014, and when the China-US trade dispute led to the collapse of oil prices after 2018. We show that information spillovers between carbon finance and shipping are bidirectional and asymmetric. The carbon finance market is the principal transmitter of information. Our results and their interpretation provide guidance to governments on whether (and how) to include shipping in emissions trading schemes, supporting at the same time the environmental sustainability decisions of shipping companies.

This paper uses high frequency option data to investigate the information content of option trading of AH cross listed stocks (A-shares traded in mainland China and H-shares traded in Hong Kong) and the role of the Shanghai-Hong Kong Connect in this issue. Measuring the informed trading with order imbalance, we find that the order imbalance of stock options traded in Hong Kong contains incremental information that predicts the return of corresponding A-shares traded in Shanghai after controlling for the cross-market return and volume factors proposed by Gagnon and Karolyi (2009). More important, this predictive power strengthens after the Shanghai-Hong Kong Connect, which is also supported by the evidence of comparison between the two stock crashes exactly before and after the connection. During the 2015 stock crash, the spillover effect of the two markets is significantly stronger than that during the 2008 financial crisis.