Accruals quality,underwriter reputation,and corporate bond underpricing:Evidence from China

This study examines the relationship between accruals quality and the underpricing of corporate bonds in China and how underwriter reputation affects this relationship.We?nd that(1)accruals quality is negatively associated with the magnitude of bond underpricing and(2)the impact of low accruals quality on underpricing is partially offset by hiring reputable underwriters.A path analysis shows that approximately 11% of the effect of accruals quality on underpricing is attributable to the indirect path through reputable underwriters,suggesting that accruals quality is more effective than reputable underwriters in lowering bond underpricing.These?ndings are signi?cant for initial bond offerings,but not for secondary bond offerings.We also?nd that low accruals quality is associated with more restrictive non-price contract terms such as greater collateral requirements and stricter covenants.

The Extreme Machine Learning Actuarial Intelligent Agricultural Insurance Based Automated Underwriting Model

The paper presents an innovative approach towards agricultural insurance underwriting and risk pricing through the development of an Extreme Machine Learning (ELM) Actuarial Intelligent Model. This model integrates diverse datasets, including climate change scenarios, crop types, farm sizes, and various risk factors, to automate underwriting decisions and estimate loss reserves in agricultural insurance. The study conducts extensive exploratory data analysis, model building, feature engineering, and validation to demonstrate the effectiveness of the proposed approach. Additionally, the paper discusses the application of robust tests, stress tests, and scenario tests to assess the model’s resilience and adaptability to changing market conditions. Overall, the research contributes to advancing actuarial science in agricultural insurance by leveraging advanced machine learning techniques for enhanced risk management and decision-making.

Artificial Intelligence-Based Automated Actuarial Pricing and Underwriting Model for the General Insurance Sector

The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the Automated Actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive Automated Actuarial Loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing Automated Actuarial Underwriting models tailored to specific policyholder categories.

Securities regulation and implicit penalties

The extant literature offers extensive support for the significant role played by institutions in financial markets,but implicit regulation and monitoring have yet to be examined.This study fills this void in the literature by employing unique Chinese datasets to explore the implicit regulation and penalties imposed by the Chinese government in regulating the initial public offering(IPO) market.Of particular interest are the economic consequences of underwriting IPO deals for client firms that violate regulatory rules in China's capital market.We provide evidence to show that the associated underwriters' reputations are impaired and their market share declines.We further explore whether such negative consequences result from a market disciplinary mechanism or a penalty imposed by the government.To analyze the possibility of a market disciplinary mechanism at work,we investigate(1) the market reaction to other client firms whose IPO deals were underwritten by underwriters associated with a violation at the time the violation was publicly disclosed and(2) the under-pricing of IPO deals undertaken by these underwriters after such disclosure.To analyze whether the government imposes an implicit penalty,we examine the application processing time for future IPO deals underwritten by the associated underwriters and find it to be significantly longer than for IPO deals underwritten by other underwriters.Overall,there is little evidence to suggest that the market penalizes underwriters for the rule-violating behavior of their client firms in China.Instead,the Chinese government implicitly penalizes them by imposing more stringent criteria on and lengthening the processing time of the IPO deals they subsequently underwrite.

Does reputation of sponsors matter in IPO?Evidence from Hong Kong

Contrary to other markets where underwriters perform a combined role of underwriting and sponsoring in an Initial Public Offering(IPO),IPO issuers in Hong Kong must appoint at least one sponsor in addition to the underwriters.The spitting of the single role of underwriters into two separate ones offers an ideal setting to disentangle the effects of the two roles and to examine which of the two roles-sponsor or underwriter--is more important in explaining IPO underpricing and initial volatility in the Hong Kong equity market.Interestingly,our findings provide supportive evidence that the sponsor reputation does matter in an IPO and it is even more significant than the underwriter reputation in explaining the IPO underpricing phenomenon.Given the recent high-tech fervor,our research goes deeper to examine specifically the role of sponsors on high-tech firms,with results indicating that the reliance on sponsors is higher for traditional isuers than for technology firms.We further discover that sponsors and underwriters are playing substitution roles rather than complementary roles.In order to examine the regulatory policy impact,our research also compares the role of IPO sponsors before and after the launch of the new sponsor regulatory regime in 2013.The empirical findings lend support to our argument that after the launch of the new regulations,public awareness of sponsors is raised,respect towards more reputable sponsor increases,and thus,the role of sponsors becomes more impotant than before.

IPO Pricing Efficiency in China： A ChiNext Board Focus

This paper examines what determines the offer price for a ChiNext IPO and discusses how we can improve the current ＂Chinese-style＂ bookbuilding process. We establish that the ChiNext IPO underwriter relies upon the institutional investors to discover the issuer＇s intrinsic value （in the form of a preliminary price）, and that the same underwriter adjusts the preliminary price to establish the final offer price, based on its assessment of the institutional investors＇ motivations. Since the underwriter does not have discretionary power in new share allocation, this ＂Chinese-style＂ bookbuilding process contains certain pitfalls from an information asymmetry standpoint. The institutional investors mainly use ＂simple and direct＂ variables that do not adequately reflect the issuer＇s true intrinsic value to develop the preliminary price, while the underwriter adjusts that price downward to establish the offer price to clear the market, as a measure to counter a perceived free-rider issue among the institutional investors. This process, in effect, contributes to initial IPO underpricing and causes principal-agent conflicts between the underwriter and the issuer. We argue that such a pricing inefficiency could be improved by an innovative ＂bookbuilding plus price discretionary auction＂ process, which is a combination of the modified OpenlPO and Taiwan-style auctioned IPO approaches.