In this paper, we examine the prediction performance using a principal component analysis (PCA). In particular, we perform a PCA to identify significant factors (principal components) and then use these factors to form predictions of stock price movements. We apply this strategy on the Chinese stock markets. Using data from January 2, 2019 till September 16, 2021, the empirical results show substantial out-performances from the PCA-based predictions against a naïve buy-and-hold strategy and also single time-series predictions of individual stocks. Next we examine if the factors retrieved from PCA are indeed important contributing factors in explaining stock price movements. To do this, we adopt a machine learning technique popular in studying stock performances – random forest. We discover that, comparing to widely used descriptive factors such as industry sector, geographical location, and market types (known as “board” or “ban” in Mandarin), principal components rank very highly among those descriptive factors.

We examine the effect of a regulation in China that restricts perquisite consumption by managers of state-owned companies. We find that the regulation causes state-owned companies to issue more entrusted loans to other firms. Furthermore, entrusted loans issued by state-owned companies have lower interest rates and larger loan amounts. These results suggest that managers of state-owned companies use entrusted loans to extract personal benefits to compensate for the lost perquisite consumption due to the regulation.

Quantifying the economic loss from climate change in China is crucial for understanding the potential costs and benefits of climate policy within the context of carbon neutrality. This study develops a multidisciplinary and integrated assessment framework for climate damage, which uses the Beijing Climate Center Simple Earth System Model (BCC-SESM) to estimate climatic data under the Representative Concentration Pathways (RCPs) scenarios with the medium Shared Social-economic Pathway (SSP2) scenario in China. This paper estimates climate damage in eight major sectors by a bottom-up approach, makes substantive revisions and calibrations for the sectoral climate damage functions and parameters for China based on the FUND model, and formulates the aggregate climate damage function. Results show that under the Business-as-Usual RCP8.5 scenario, by 2050 human health damage accounts for the largest share (61.92%) of the total climate loss, followed by sea-level rise damage (18.57%) and water resources damage (5.84%). Climate damage in non-market sectors reaches 14.64 trillion CNY, which is a 4.8-fold increase over the climate damage of market sectors which is only 3.02 trillion CNY. The total climate damage function for China is a quadratic function of temperature rise, with climate damage of 5.36%, 5.67%, 5.74%, and 8.16% of the GDP by 2050 under RCP2.6, RCP4.5, RCP6.0, and RCP8.5 respectively, indicating that the marginal climate damage increases non-linearly with temperature rise.

We estimate the causal effect of income on happiness using a unique dataset of Chinese twins. This allows us to address omitted variable bias and measurement errors. Our findings show that individual income has a large positive effect on happiness, with a doubling of income resulting in an increase of 0.26 scales or 0.37 standard deviations in the four-scale happiness measure. We also find that income matters most for males and the middle-aged. Our results highlight the importance of accounting for various biases when studying the relationship between socioeconomic status and subjective well-being.

This study examines the relationship between analyst forecast dispersion or accuracy and supplier concentration of listed firms in China from 2008 to 2019. Our findings suggest that higher supplier concentration is associated with lower analyst forecast dispersion, which can be attributed to the increased attention it receives from analysts. Moreover, this effect is more pronounced when firms have less bargaining power and higher institutional ownership, indicating a greater reliance on the supply chain. Our study highlights the importance of disclosing supply chain information, which provides insight beyond traditional financial information.

Evidence from the U.S. and Europe suggests that current risk assessment tools used by researchers and financial professionals to determine individuals’ risk tolerance and provide suitable portfolio recommendations may be flawed due to “mis”perceptions of risk. Limited research has examined the reliability of these tools as measures of relative risk tolerance for households in emerging economies like China. This study develops a multidimensional index of risk tolerance specifically tailored for Chinese households using a psychometric approach. The effectiveness of this multidimensional index in predicting individuals’ financial decisions is tested and compared to traditional unidimensional measures of risk tolerance commonly used in developed countries. The findings indicate that multidimensional measures are more consistent and significant predictors of Chinese households’ investment decisions. Additionally, the study uncovers evidence that cultural differences, related to market expectations and social networks, which are often overlooked in U.S. and European models, play a crucial role in shaping individuals' risk perceptions and investment choices in China. Robustness checks were conducted to account for potential endogeneity between risk tolerance and investment decisions. The findings provide valuable insights for researchers and financial professionals seeking to develop more accurate risk assessment tools that capture risk attitudes and perceptions in China and other developing countries. By adopting a multidimensional approach that accounts for cultural and psychosocial factors, these improved tools can enhance the precision of risk evaluation and facilitate more appropriate investment recommendations.

This study examines whether sell-side analysts have the ability to disseminate information consistent with anomaly prescriptions in China. I adopt 192 trading-based and accounting-based anomaly signals to identify undervalued and overvalued stocks. Analysts tend to give more (less) favorable recommendations and earnings forecasts to undervalued (overvalued) stocks. On analyzing the information content, I find that analyst recommendations and earnings forecasts are consistent with accounting-based information rather than trading-based information. Analysts make recommendations and earnings forecasts consistent with anomalies, especially when firms experience relatively bad firm-level information. Additionally, undervalued (overvalued) stocks are associated with high (low) analyst coverage. The results indicate that analysts may contribute to mitigating anomaly mispricing and improving market efficiency in China.

This study investigates the association between the expansion of formal finance through the opening of high-speed railway (HSR) and the risk of private lending in China, using data from China Judgments Online. The study employs a difference-in-differences approach and reveals that the cities connected by HSR have a greater risk of private lending and that the formal finance expansion leads to a cream-skimming effect on private lending. The HSR enhances the efficiency of product supply and pricing of formal financial institutions, which results in the expansion of banks. However, private lending relies heavily on social networks, which limits the direct effect of HSR on it. Consequently, the formal finance expansion facilitated by HSR has a significant cream-skimming effect on private lending, which increases the risk of private lending. These results make a significant contribution to the existing literature on the economic implications of formal financial expansion for private lending.

This paper investigate whether and how local climate warming affects urban-rural income inequality. Our empirical results reveal that a rise in Climate warming risk leads to an increase in urban-rural income inequality, which is largely unaffected by a battery of robustness checks and endogeneity concerns. The analysis of economic mechanisms shows that climate warming risk impacts urban-rural income inequality mainly by reducing the output of the primary sector. Notably, we uncover evidence that the amplifying effect of climate warming risk is not homogeneous across the cross-section, particularly pronounced in prefectures with lower urban-rural integration and poorer rural financial services, but with high share of rural population. Overall, our research confirms the notion that climate warming risk has an important implication in shaping Chinese urban-rural income inequality.

The growth of digital economy and sustainable development of environment are important issues related to high-quality economic development in the new era. This paper selects the yearly data of China from 2007 to 2021, constructs the China’s Environmental Performance Index, and establishes the TVP-VAR model to investigate the dynamic time-varying relationship between digital economy growth, CO2 emissions, and sustainable development of environment in short, medium and long-term. The results show that the relationships among them are time-varying at all terms. Specifically, in first, the growth of the digital economy exerts a negative impulse on CO2 emissions, and the short-term effect is greater than the long-term effect. Secondly, there exist positive impulses between the growth of the digital economy and sustainable development of environment. And CO2 emissions has a negative impact on sustainable development of environment. Thirdly, they have same influencing tendencies at certain time points, but different impact degrees. The impact of the digital economy development on environmental sustainable development has significantly increased since the COVID-19 outbreak. Therefore, the development of digital economy can effectively reduce CO2 emissions and promote the sustainable development of the environment.