The Mainland-Hong Kong Stock Connect program is a globally unique institutional innovation. This partially open financial system is unparalleled worldwide. As the influence of the Mainland-Hong Kong Stock Connect on A-shares has grown, the volume of research literature has gradually increased, and studies on the policy impact from various sectors have become prevalent. Prior to the introduction of the Mainland-Hong Kong Stock Connect, studies indicated that A-share stock prices did not significantly react to stock information, indicating low informational content in stock prices. The Mainland-Hong Kong Stock Connect, through its moderate openness, has effectively introduced mature overseas investment philosophies and international capital, altering the investor structure of A-shares and impacting trading behavior. This paper aims to explore whether the initiation of the Mainland-Hong Kong Stock Connect policy positively affects the informational content of A-share stock prices under the aforementioned premises. To minimize the interference of short-term market fluctuations on the research, this paper uses the relatively long-term future earnings response as the entry point for studying the informational content of stock prices. Specifically, it first selects a full sample of Mainland-Hong Kong Stock Connect stocks to conduct annual cross-sectional regression and multi-year linear regression to examine changes in the informational content of stock prices before and after policy implementation. It then includes a control group of stocks not selected for the Mainland-Hong Kong Stock Connect, conducting multi-year linear regression analysis with the experimental group samples to investigate whether the policy initiation has improved the informational content of stock prices for Mainland-Hong Kong Stock Connect stocks compared to those not selected. The results show that after the initiation of the Mainland-Hong Kong Stock Connect policy, the informational content of stock prices increased for Shanghai Stock Connect but decreased for Shenzhen Stock Connect. Compared to stocks not selected for the Mainland-Hong Kong Stock Connect, the informational content of stock prices also increased for Shanghai Stock Connect and decreased for Shenzhen Stock Connect. Overall, the results of this study indicate that the Mainland-Hong Kong Stock Connect policy has indeed achieved its initial policy design goals, warranting further exploration into deepening openness to optimize the structure of the capital market.

This article uses the S&P 500 index as an example to analyze the impact of macroeconomic factors on stock returns. By using the S&P 500 index data from 1968 to 2020 as the dependent variable, and the monthly data of 221 macroeconomic variables such as the consumer price index and the US mid-term election as the independent variable, this paper finds that: (1) a wavelet denoising method helps to capture the low-frequency and long-term fluctuations in monthly returns of the index, which can effectively remove the short-term fluctuations in returns, better reflect the macroeconomic trend, and improve the power of out-of-sample forecasting. (2) the Granger causality test may be used to pick the top 30 most significant variables, which can be incorporated into several prediction models. Among all the prediction models, the combined prediction algorithm has the best out-of-sample prediction effect. (3) investors need to consider investment practices under timing strategies. Elastic network, scaling principal component analysis, combination prediction, and other algorithms are used to select the time, and the best results are obtained based on the scaling principal component analysis algorithm and the combination prediction algorithm when the transaction fee is set to 5‰. The returns based on these two algorithms have reached 14.00% and 12.59%, their Sharpe ratios are the highest among all algorithms, reaching 0.69 and 0.62, respectively, and this result is significantly better than the historical mean model used as a measurement benchmark (with the average return of 8.14%, and aii Sharpe ratio of 0.34). (4) explore investment practice under a stock-picking strategy. We use methods such as sector rotation strategy and mean-variance of sectors for stock selection, and find that the strategy returns achieved by investing in stocks using the sector rotation strategy are the best, reaching 14.30%, and the Sharpe ratio is the highest at 0.79, significantly better than the benchmark S&P 500 index (with the average return of 8.8% and a Sharpe ratio of 0.57).

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.

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.

Non public offerings are common economic activities of enterprises. Enterprises that make non-public offerings can use the financing capital to purchase fixed assets and expand. From the perspective of enterprise innovation, with the continuous upgrading of market competition, enterprises may need new capital to upgrade their assets, or purchase key technology licenses, patents, and invest in start-ups in related industries. Therefore, there may be some correlation between economic activities and innovation. It is of great significance to understand the impact of economic activities and analyze the influencing factors of innovation performance for how to better promote innovation. This paper increases the consideration of the impact of non-public issuance on innovation, and further find out the impact mechanism of innovation. Although there is positive relationship between non-public issuance and invention patents and utility models, with the relationship with utility models is significant at the 5% level, considering the endogeneity of this relationship remains significant only in the DID model. In the sub samples of non-public issuance plans, the success of issuance also becomes insignificant. Under the instrumental variable method, the conclusion of the benchmark model is supported, and in the sub samples of non-public issuance plans, the success of issuance is still not significant. In the lag phase inspection, non-public issuance promotes the improvement of utility model patents in enterprises. The asset liability ratio of enterprises has certain positive moderating effect on the relationship between non-public issuance and corporate innovation, but this effect only holds across the entire sample range. In the subsample, this conclusion is not true. The moderating effects of R&D ratio, whether there are cash transactions, and whether major shareholders participate are not significant. Companies with low public offering price ratios, i.e. those with high discounts, may experience a decrease in their innovation after successful issuance. These companies are relatively inferior in the market and attract non-public investors with high discounts. They cannot create good innovation, while good innovative companies have lower discounts on non-public offerings. After raising funds, companies increase their R&D, Promote further innovation achievements.

With the continuous development of the Chinese capital market over the past thirty years, the securities analyst industry has experienced a process of transformation from a reckless period to a golden time. One of the most important signals is that securities analysts are increasingly conducting research report providing long-term earnings forecasts for the company. However, current research on analysts is limited to their short-term forecasting behavior, and there is little on analysts' long-term earnings forecasts. Therefore, this article takes the research on analysts' long-term forecast reports issued by analysts on A-share listed companies, and conducts an empirical study on the analysts' forecasts accuracy and its influencing factors. First, the author combed the research literature related to analyst forecasts and selected variables from three dimensions, including company characteristics (financial indicators and non-financial indicators), analyst characteristics and affiliated institution characteristics; secondly, considering the high-dimensionality of the influencing factors, this paper uses the method of combining machine learning and traditional regression to conduct empirical research; finally, the research tested the heterogeneity of influencing factors from two perspectives, including time and industry.The results of this article show that the long-term profit forecasts of analysts in China have advantages over traditional statistical models. More than 60% of analysts provide profit forecasts that are better than statistical models. Afterwards, when examining the factors that affected the accuracy of analysts’ forecasts, it found that although analyst and institutional characteristics affected analysts’ predictions to a certain extent, company characteristics are the most important variables among them all. As the time goes by, the influence of non-financial factors on forecast accuracy gradually decreasing, but analyst characteristics continue to strengthen. In addition, cyclical industries are more difficult to predict than companies in non-cyclical industries, and the difficulty of prediction will not be reduced with the analyst efforts. This research can help analysts optimizing their forecasting behavior and prompts investors to understand analysts' reports more deeply, which makes them using analyst forecast data to make investment decisions in a rationally ways, and it can also help to promote the securities pricing efficiency and development of Chinese capital market.

This study focuses on China's mutual fund market and analyzes the impact of fund managers' past experience on their performance. The research results show that fund managers with management experience, including those who have held senior management positions, can significantly improve performance. This may be due to their excellent team coordination, strategic planning, and decision-making abilities. In contrast, managers with professional technical or accounting backgrounds may reduce performance, possibly because they rely too much on past investment experience, lack decision-making flexibility, and are unfamiliar with corporate management. For management-type fund managers, the postgraduate education of the fund manager and the number of award-winning funds of the fund company have a positive moderating effect on performance. However, factors such as the number of funds managed, the amount of assets under management, the number of managers and employees in the fund company, and the amount of newly issued funds may have a negative impact. These findings provide valuable references for fund managers and fund companies in improving performance, and have important implications in various aspects such as selecting fund managers, setting investment strategies, and managing the number and size of funds.

The pharmaceutical industry plays an important role in Chinese economic development. How to propel the innovation and growth of the pharmaceutical industry with capital market tools becomes an important question. With the increasing layout of venture capital (VC) in the pharmaceutical industry, the impact of VC participation on the innovation and growth of pharmaceutical enterprises should be carefully studied. For investment purposes, CVCs, which are established by industrial corporations, mainly focus on new technologies and modes in the relevant industrial chain of their parent companies, and try to establish strategic synergy through their equity investment. IVCs have no specific industry restrictions, and their purpose is to search for and identify innovative enterprises with high growth potentials, and to harvest financial gains by investing in them.In order to explore these issues, this paper collects and analyzes data from a sample of Chinese A-share listed pharmaceutical companies from 2015 to 2022, tests the impact of VCs on the innovation inputs, innovation outputs, and growth performance of this sample of companies, and examines the differences between the impacts of IVCs and CVCs on the relevant performance. It is found that VC investment has a significant positive impact on pharmaceutical firms' innovation input, innovation output, and firm growth. In particular, IVCs have a significant positive effect on innovation input, growth performance, and an insignificant effect on innovation output of pharmaceutical firms. CVCs, on the other hand, have a significant positive effect on innovation input, innovation output, and growth performance of pharmaceutical firms. In addition, the interaction between IVC and CVC can further enhance the innovation input level and growth performance level of pharmaceutical enterprises. This paper uncovers the differences in the impact of IVCs and CVCs on the innovation input, innovation output, and growth performance of pharmaceutical enterprises, expands the research on venture capital, enriches the driving mechanism of pharmaceutical enterprises' high-quality growth and innovation capability in the Chinese context. This paper also provides some insights into how pharmaceutical enterprises select VCs and how VCs can empower pharmaceutical enterprises in practice.

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.

With the ongoing development of China's financial market, the investment choices for investors are gradually enriched. Exploring asset allocation for different economic cycle stages can help investors achieve higher returns from the economic cycle rotation, and at the same time, effectively diversify the investment risks and improve the stability of investment returns. In this paper, we systematically sort out a series of studies on asset allocation and economic cycle theory, and build an economic cycle rotation investment strategy applicable to China's economic environment and changes in China's capital market.Based on China's macroeconomic data and investment asset classes, this paper optimizes the division of economic cycle stages, integrates the economic cycle rotation strategy and risk parity strategy, and incorporates liquidity elements to construct an asset allocation strategy. Specific findings are as follows: (1) this paper uses the "slope" and "threshold" of the year-on-year change of industrial value added to divide the economic output stage, which overcomes the drawbacks of relying on economic cycle indicators that cause frequent changes in cycle stages; (2) the investment strategy developed in the paper is able to obtain considerable investment returns, reduces investment risks, and achieves retracement control. ii The findings of this paper enrich and expand the research on economic cycle theory and asset allocation theory to a certain extent, and also provide some inspiration for the practice of asset allocation.