Government Regulation,Enforcement,and Economic Consequences in a Transition Economy:Empirical Evidence from Chinese Listed Companies Implementing the Split Share Structure Reform

In a changing transition economy, Chinese government regulations that adopt the relatively simple bright line rule formula are enforceable in practice. Taking the early reform-oriented policies of the China Securities Regulatory Commission(CSRC) as an example, we find that the CSRC did not consider local enthusiasm for reform when allocating IPO resources because of the high enforcement costs involved. We also find that CSRC listed company regulations were enforced due to the lower costs involved in verifying regulatory violations, and that listed companies that completed the reform process were given priority in public refinancing. We present empirical evidence supporting the theoretical basis for the hypotheses outlined above. We also conclude that companies that completed the reform process in 2005 were of significantly higher quality and that the SEO regulation did not affect stock market efficiency. These findings enhance our understanding of the efficiency of government regulation in a transition economy.

DEEP NEURAL NETWORKS COMBINING MULTI-TASK LEARNING FOR SOLVING DELAY INTEGRO-DIFFERENTIAL EQUATIONS

Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.

State ownership and firm performance: Empirical evidence from Chinese listed companies

While the relationship between state ownership and firm performance has been widely researched, the empirical evidence has provided mixed results. This study applies panel data regression techniques to 10,639 firm-year observations of nonfinancial Chinese listed firms during 2003–2010 to examine the relationship between state ownership and firm performance. The results show that state ownership has a U-shaped relationship with firm performance. The Split Share Structure Reform in2005–2006 played a positive role in enhancing the relationship between state ownership and firm profitability ratios. Although state ownership decreased significantly after 2006, it remains high in strategically important industry sectors such as the oil, natural gas and mining sector and the publishing, broadcasting and media sector. The findings reveal that a higher level of state ownership is superior to a dispersed ownership structure due to the benefits of government support and political connections. The Split Share Structure Reform made previously nontradable shares legally tradable, improving corporate governance and reducing the negative effect of non-tradable state shares.