Kungfu bonds are bonds denominated in dollars issued by mainland companies in overseas markets. In the past ten years, the issuance of Chinese dollar bonds has been in full swing. The Kungfu bond market is booming with China's economic development and the deepening of the capital market. Since 2019, the regulatory policies for the domestic real estate industry have gradually become stricter. Developers led by Evergrande and Country Garden have extremely tight cash flow, and their domestic and overseas financing channels are greatly restricted. In addition, due to the repeated impact of the epidemic, the risk exposure of Chinese-funded US dollar bonds and real estate debts has intensified. Due to the wave of defaults induced by the forced deleveraging of domestic regulators, overseas credit bonds with poor credit quality have been sold by investors. This paper looks into the interest rate level of Kungfu bonds as the research object, and examines the information asymmetry as the research entry point to conduct an in-depth quantitative study of the additional costs that mainland real estate companies need to pay for new bonds issued in the international market, and what factors may exacerbate or alleviate information asymmetry.This study found that Mainland real estate companies need to pay an additional 1.2238% interest rate difference on average when issuing bonds in Hong Kong for the first time. In addition, Mainland real estate companies that have issued bonds in the international market pay a lower credit premium on average, which means that issuing bonds in the international market can significantly enhance the company's reputation and alleviate information asymmetry among institutional investors, thereby reducing financing costs. To sum up, this paper analyzes in depth the pricing problem of Chinese dollar bonds issued in the international market through the method of quantitative regression, enriches the related research on bond issuance pricing, and provides information necessary for practitioners to make investment decisions and for listed companies to make financing arrangements. It provides valuable suggestions, which may be applied to other industries.

Based on the common phenomenon of style rotation in domestic and foreign stock markets, this paper aims to study and answer which factors jointly drive style rotation and whether style rotation is predictable. Based on the dividend discount model, this paper selects variables that may explain style rotation from the three dimensions of capital cost, risk premium and performance growth. At the same time, this paper innovatively introduces the capital flow variables of institutional investors and northward funds to help explain and predict style rotation from the perspective of "smart money".First, based on the A-share market, this study uses the daily frequency value factor yield data from January 1, 2015 to December 31, 2020 to carry out temporal regression of the variables that may affect the value factor yield. It is found that the macroeconomic leading indicator and Wind A dynamic dividend yield can significantly affect the yield of value factor, and the impact is positive, that is, the rise of the macroeconomic leading indicator and the rise of the dynamic dividend yield of A shares both lead to the rise of value factor yield. In addition, based on daily frequency, this paper also found that value factor yield and northbound capital scale is significantly negatively correlated, but this relationship does not exist on monthly frequency. Secondly, this study further uses the daily frequency value factor yield data from January 1, 2015 to December 31, 2020 to carry out temporal regression of each explanatory variable of the previous day, trying to study whether these variables can predict the value factor yield. It is found that the leading macroeconomic indicator and Wind All-A dynamic dividend yield can positively predict the yield of value factor. Specifically, if the leading macroeconomic indicator rises in the previous trading day or the Wind A dynamic dividend yield rises in the previous day, on average, the value factor yield will rise in the next trading day. This finding is consistent with the results of synchronous temporal regression in the previous section. In addition, this paper does not find that the size of northbound funds can significantly predict the return rate of the value factor. Finally, this study uses variables that have significant predictive effect on the value factor rate of return to build a model. Based on this model, the out-of-sample value factor rate of return is predicted, so as to timing the value factor. The results show that the rate of return of value factor investment strategy based on model timing is twice as high as that of long-term holding value factor.

Convertible bonds, as a vital means for listed companies to raise funds, are favored by both listed companies and institutional investors due to their hybrid features of equity, debt, and options. The U.S. market, with its large scale, significantly supports the rapid growth of numerous high-tech enterprises. In contrast, China's convertible bond market started later and lags behind in terms of issuer numbers and issuance scale. Since the issuance regulations on February 17, 2017, the convertible bond market in China has seen substantial development opportunities, with increased enthusiasm from listed companies and various investors, leading to significant growth in market issuance and trading volume.This article employs Monte Carlo simulation to empirically study the theoretical pricing of convertible bonds, aiming to identify factors influencing differences between theoretical and market prices. Analyzing classic terms such as conversion, redemption, put, and call provisions, the study establishes a foundation for subsequent convertible bond pricing research. Following the consideration of various terms, the article uses the Monte Carlo simulation method to attempt pricing the asset prices of convertible bonds. To enhance computational efficiency, the core simulation process undergoes encapsulation and optimization. The analysis of theoretical prices reveals minimal overall fluctuation in pricing errors, showing a negative error rate before June 2021, indicating market prices slightly below theoretical prices, and afterward, market prices slightly above theoretical prices. Finally, the article analyzes the reasons behind pricing errors, examining 13 variables' potential impact through regression analysis, finding significant effects from 11 variables.The article attempts to provide a reasonable interpretation of the logic behind the impact of variables on pricing errors.

This study aims to explore the impact of employee incentives on innovation in the Chinese inductive manufacturing industry. Using a sample of publicly listed inductive manufacturing companies in China, we construct a panel dataset spanning from 1994 to 2022 and employ a multiple regression model for empirical analysis. Our findings reveal that employee incentive programs have a significant positive effect on the performance of inductive companies in terms of inductive reliability experiments, and the number of patent applications, granted patents, and patent citations over the next two years. Particularly, the positive relationship between employee incentives and innovation is more pronounced in companies with higher ownership concentration. This study provides empirical evidence supporting the crucial role of employee incentives in facilitating corporate innovation in Chinese inductive manufacturing firms. Furthermore, the results provide valuable insights for firms in formulating stock ownership structures and employee incentive plans, as well as policy implications for developing China's high-end manufacturing industries.