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.

Using data from Chinese A-share listed firms from 2015 to 2022, a difference-in-differences model is employed to empirically examine the impact of bankruptcy regimes, marked by the establishment of the bankruptcy court, on firms’ total factor productivity (TFP). The results show a significant decline in TFP among firms in regions following the establishment of the bankruptcy court. This result remains valid after a series of robustness tests. Mechanism tests reveal that bankruptcy court heightens firms’ risk aversion by endowing excessive rights to creditors. Consequently, firms tend to downwardly adjust capital structure, curtail innovation investment, and accumulate liquid assets as coping measures, ultimately contributing to a decline in TFP. However, well-developed market mechanisms can alleviate the negative impact of bankruptcy court excessively protecting creditors. Specifically, when firms are located in regions with weak government intervention and strong financial development, as well as in market environments with low uncertainty and strong competition, this negative impact can be mitigated. Moreover, we find that under bankruptcy court operations, while a series of risk reduction measures taken by firms triggers a decline in TFP, it mitigates the risk of financial distress. These findings provide fresh insights into the dual nature of creditor protection and offer valuable references for governments to improve the bankruptcy legal system.

This study examines the impact of climate-transition risks on trade credits for Chinese listed companies from 2007-2017. We develop an index of county-level climate-transition risks faced by Chinese-listed companies using data on local carbon emissions and carbon sequestration when moving towards net zero carbon emissions. Our two-way fixed effects OLS regression results find that local firms facing greater climate-transition risks significantly reduce their trade credit financing. Specifically, a one standard deviation of increase in Risk leads to a 0.73% decrease in trade credit. This reduction is more pronounced for state-owned enterprises (SOEs), firms operating in less competitive industries, and those headquartered in regions without carbon trading markets. Our main finding is robust to a battery of sensitivity tests including the use of alternative measures and lagged independent variables. Results on an Instrumental Variable (IV) method and a differences-in-difference (DiD) analysis suggest a causal relationship between climate-transition risks on trade credit. Further analyses reveal two plausible channels for the effect: increased financial distress risk and enhanced access to bank credit.

Whilst there is increasing research attention on predicting financial distress, the existing literature is subject to two specific limitations. The first is that a firm can experience a financial distress event (e.g., loan default, bankruptcy) more than once, yet most studies that model corporate financial distress prediction treat financial distress as occurring only once. This approach leads to an inefficient use of data with all subsequent events being ignored and subsequently a decrease in statistical power. Second, to account for the lack of independence between observations of repeated event data, the extant research utilising hazard analysis either has a separate analysis for successive distressed events or relies upon robust standard errors. In addition to a much smaller sample, a separate analysis yields the models that can be used to predict the survival of a distressed firm rather than the survival of a firm generally. The method of robust standard errors, while innocuous to one-time event data, ignores the possible downward bias in coefficient estimates for repeated event data. To address these two limitations, we treat financial distress as repeated events and apply more advanced methods (generalised estimating equations, random effects, fixed effects, and a hybrid approach) to account for the lack of independence between observations in discrete time hazard analysis. These different approaches are applied to a sample of listed companies in China over the 2007‒2021 period. We find that variables that are not statistically significant in models based on one-time events data become statistically significant in the models based on repeated events data, and that coefficient estimates are larger in their magnitude with more advanced methods than with the method of robust standard errors. We also find that among the advanced methods, a hybrid approach achieves substantially better out-of-sample prediction, particularly over a long-term horizon than other approaches. Our results remain robust in tests of robustness.

Shadow financing through off-balance sheet wealth management products (WMPs) has become increasingly important besides deposits in China. We quantify the economic magnitude of the effect of WMPs on banking stability in an equilibrium model calibrated to Chinese banking sector data. Alternative equilibria emerge, which deviate substantially from the observed banking system and lead to severe financial distress and large welfare losses. Rollover costs from the WMP market and negative shocks to the asset market underlying WMPs can exacerbate banking instability. Moreover, we show that smaller and medium sized banks are comparably relevant for financial stability as the systemically important big 4 banks in China.

Friedman et al. (2003) developed a model in which, in equilibrium, controlling shareholders may choose either tunneling or propping depending on the magnitude of an adverse shock and the magnitude of the private benefits of control. In this paper, we employ connected transaction data from China to test the implications of their model. We hypothesize that, when listed companies are financially healthy (in financial distress), their controlling shareholders are more likely to conduct connected transactions to tunnel (prop up) their listed companies and the market reacts unfavorably (favorably) to the announcement of these transactions. Our empirical findings strongly support our hypotheses. Our analysis supports Friedman et al.’s (2003) model by furnishing clear evidence that it is possible that propping and tunneling might occur in the same company but at different times.

We examine whether the gains from incentive realignment have driven corporations out of the public security market. It is shown that going private transactions are due to the reduction in the diversification gains from the public market. The anticipated gains from incentive realignment are likely to be lowest among firms whose managers own most equity and the leverages are high. Avoiding the high cost of being public is the primary consideration for managers to take a firm private. Such firms go private because of financial distress and dwindling profitability. These kinds of going-private activities are counter-cyclical. On the other hand, a less distressed firm with diffused ownership has high anticipated incentive gains. The gain from incentive realignment is the dominant factor for these going-private transactions. Such firms go private because of an increase in profitability or an improvement in financial distress. We show that these going-private activities are pro-cyclical.

Many prior studies have been devoted to financial distress of Chinese listed companies over the last two decades. However, these distressed companies are still failed to find out the exact determinants of financial distress. Therefore, the purposes of this paper are to provide an investigation of financial distressed companies trading on Chinese Stock Exchanges, and to elaborate the determinants of falling into financial distress by using a panel data set containing information on the stock market under Binary Logit Model during the period 1998-2005. The empirical findings present the relationship between 13 independent variables and the probability of financial distress, and particularly analyze the impact of corporate governance on Chinese financial distressed companies. Of these corporate governance variables, agency costs and ownership structure appear to be important factors to affect the probability of financial distress.