Algorithmic trading has emerged in the A-share market in recent years, and its impact on capital market pricing efficiency has received wide attention across industry and academia. This paper focus on companies listed on the SZE Growth Enterprises Market and SSE STAR Market, aiming to explore the influence of algorithmic trading on the information content of stock prices before the release of quarterly earnings announcements. Firstly, by considering the trading system features and investor structure characteristics of the A-share market, this paper constructs algorithmic trading indicators tailored to the A-share market. Based on these indicators, empirical tests reveal that algorithmic trading reduces the information content of stock prices before earnings announcements, indicating that the liquidity demand strategy of algorithmic trading plays a dominant role. Further mechanism analysis shows that the negative impact of algorithmic trading on stock price information content stems from increased transaction costs for slower investors and reduced large orders from informed traders. Lastly, the research finds that while algorithmic trading exhibits a crowding-out effect on informed traders, it also mitigates abnormal stock price fluctuations caused by noise trading. This study deepens our understanding of the economic consequences of algorithmic trading at the market level and provides insights for regulators to improve related policies concerning algorithmic trading.
With the ongoing emergence of global infectious disease virus variants and the increasingly severe biological security situation, the accuracy and efficiency of infectious disease detection have become crucial components in monitoring virus spread and protecting public health. However, the reliability of detection is often challenged by factors such as technical limitations and operational standardization. This paper examines the accuracy of large-scale detection of infectious diseases. Taking the COVID-19 epidemic as an example, we calculated the false-negative predictive values of "single test" and "pooled test" based on Bayesian statistical modeling, and proposed optimal testing strategies for different areas and sensitivities. It includes the selection of "single testing" and "pooled testing" strategies, the optimization of testing population and frequency, and adjustments to the intervals of regular testing. Furthermore, we computed the corresponding testing accuracy and optimal interval times for gender-separated testing schemes in low-risk areas. Finally, combined with the calculation results of this study, we formulated policy recommendations concerning infectious disease detection, aimed at providing a scientific foundation and policy support for effective disease prevention and control.
Since the proposal of the capital asset pricing model (CAPM), the risk-return relationship has always been a key issue in academic research. However, the current debate on the effectiveness of the CAPM model is still ongoing, and the conclusions are not unified. Based on existing studies, this paper comprehensively considers the differences in trading mechanisms, trading characteristics, and investor types between the intraday and overnight periods, innovatively introduces a time heterogeneity perspective to decompose the daily trading period into two parts, and further studies the risk-return relationship in China. Specifically, we use the portfolio-sort way and Fama-MacBeth regressions to test the relationship between systematic risk (proxied by the beta coefficient) and stock returns and find positive correlations from the daily and intraday betas. This positive relationship does not exist in overnight periods and may even display a high-risk but low-return phenomenon. The previous studies based on the daily data mainly display findings from the intraday trading period but fail to reveal the role of overnight trading, and this paper tries to supply them. In addition, the positive relationship is stronger in stocks with small market capitalization, poor liquidity, and high idiosyncratic risk, and investor sentiment and arbitrage limitation also play an essential role. Our results are robust under the adjustment of different factor models, alternative beta measurements, and various subsamples. This paper is of great importance for investors to further understand the CAPM model, understand the heterogeneous performances at three periods, and improve the risk-return evaluation framework. Our paper also helps regulators revise the related policies and pricing mechanisms and achieve effective measurement of systemic risk in China's A stock market.
This paper employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) method to study the total volatility spillover effects across nine critical financial markets impacted by the COVID-19 pandemic. These markets include crude oil, natural gas, emerging and developed country stocks, industrial metals, precious metals, cryptocurrencies, agricultural products, and foreign exchange markets. The study examines the roles and pairwise relationships of these markets during and after the pandemic. The findings indicate that the system's overall shock reached its peak at the onset of the pandemic and decreased over time. Notably, the pairwise volatility spillover analysis shows significant changes in risk contagion among the markets compared to the period before the pandemic. Specifically, the crude oil market shifted from a net spillover to a net receiver of volatility; the cryptocurrency market transitioned from a less significant role to a major net spreader of risk, whereas the agricultural products market was minimally affected by the pandemic.
This paper constructs a credit risk prediction model based on deep learning (CDL), and explores the economic mechanism behind it. Empirical result shows that CDL model can predict corporate bond credit risk more accurately compared with classical machine learning model and ordinary neural network model. Mechanism analysis shows that CDL model has stronger nonlinear prediction ability for bonds with higher relative risk. In terms of enterprise characteristics, valuation and growth indicators and intangible asset indicators are more important in the model prediction. In addition, CDL model identifies bonds with high risk by effectively identifying economic characteristics such as small trading volume, high financing constraints, and low internal control quality. This paper provides a new way to predict bond credit risk, which is helpful to maintain financial market stability and promote high-quality economic development.
The commonly used Taylor rule generally stares at the output gap and the inflation gap, but under major exogenous shocks, the Taylor rule may need to adjust the target of staring at the right time in order to improve the adjustment effect of monetary policy. This paper takes the new crown epidemic as a representative of exogenous shocks, and constructs the PVAR model in stages before and after the exogenous shocks, and comparatively studies the effectiveness of the Taylor rule after adjusting the target of focus. The study shows that the Taylor rule is effective for developed countries and floating-exchange-rate countries; moreover, the Taylor rule with the addition of exchange rate and financial stability can better cope with exogenous shocks. The policy recommendations are: To improve the international monetary policy coordination mechanism, to establish a monetary policy control framework to cope with exogenous shocks in sudden crisis events, and to implement a stable exchange rate policy and a macro-prudential control policy for cross-border capital. China should adjust its monetary policy in line with its own development characteristics, so as to realize the optimal control of the macroeconomy and resist the adverse effects of exogenous shocks.
From the perspective of the micro level, this paper constructs a variable to measure the low-carbon transition of Chinese A-share listed companies. From 2007 to 2021, the degree of low-carbon transformation of China's A-share listed companies has gradually deepened, and the number of companies participating in low-carbon transformation has gradually increased. Firms with larger sizes and higher profitability, and firms with significant carbon emissions are more likely to adopt low-carbon practices. Our empirical analysis shows that the low-carbon transition has a significantly and positive effect on firm value. This effect is stronger for firms that face stricter environmental regulations. The increasing of market attention, improving of financing constraints, easing financing constraints and the reducing of financing costs are the plausible channels that low-carbon transition affects firm value. These findings offer valuable insights for promoting green and low-carbon development and provide empirical evidence supporting the transition of firms towards a low-carbon economy.
Exploring the impact of uncertainty on the resilience of Chinese residents' consumption is of great practical significance for expanding domestic demand, smoothing the domestic cycle and economic recovery. By constructing a stochastic computable general equilibrium model of China's domestic demand market, this paper measures the impact of uncertainty shocks on the resilience of household consumption from the perspectives of aggregate and structure, and examines the guarantee mechanism of different policy combinations to expand the resilience of household consumption from the institutional perspective. The results show that income level is the most important factor affecting the resilience of residents' consumption, and the impact of multi-risk cross-infection on residents' consumption resistance is greater than the simple superposition of single factors, and the recovery shock may have a reverse impact on different economic indicators. The resilience of urban and rural residents' consumption is structurally different, and the recovery shocks on the supply side and the demand side jointly affect the resilience of the domestic demand market, among which excess supply will also cause a decline in economic benefits. To improve the resilience of household consumption and achieve the goal of expanding domestic demand, it is necessary to integrate the roles of the government and the market, and the policy guidance needs to focus on employment issues and realize the free flow of land and capital factors between regions. This paper further puts forward corresponding policy recommendations. This paper uses the method of calculable general equilibrium to explore the resilience of household consumption, which breaks through the limitations of local equilibrium in existing studies and comprehensively and systematically measures the resilience of household consumption. By introducing random numbers, the impact of uncertainty shocks on the resilience of household consumption is more effectively simulated, which provides more specific and detailed theoretical support and policy enlightenment for the expansion of household consumption.
The proportion of rent to household income is an important indicator for measuring the rental burden of urban households. Due to limitations in data availability, the ratio of annual average rent to average household income in a city is often used to study the rental burden. However, this method often leads to a general logical conclusion that low- to middle-income households bear a heavier burden, without providing quantitative results for households at different income levels. This paper utilizes grouped data on rental households from the Seventh Population Census and data on per capita disposable income of urban households. By introducing two continuous distribution functions for rent and income, it overcomes the issue of inconsistent statistical calibers and constructs a model for matching rent with household income efficiency, simulating and calculating for typical cities. This model can not only accurately calculate the overall average ratio of rent to household income in various cities but also detail the actual rental burden for households at different income levels. While revealing the differences in rental burdens among different cities and income groups, this model is also operational and universally applicable, aiding in the assessment of the fairness and effectiveness of the rental market.
The integrated development of urban and rural areas is a symbol of the modernization of a country and region, and also an inevitable requirement of Chinese path to modernization. How to effectively measure its development process and level has become a hot issue of great concern in the academic community. This article aims to comprehensively review the relevant literature on measuring the level of urban-rural integration development from an economic perspective, clarify and define the concept of urban-rural integration as well as its four dimensions, including economic, social, spatial, and ecological integration. Then summarize the existing measurement indicators, methods, and regional comparative studies of the multidimensional integration level between urban and rural areas, and sum up the focus and characteristics of various studies. On this basis, further research ideas for the construction and measurement of the indicator system for urban-rural integration development are proposed from four dimensions: The connection between objective and subjective factors, the bidirectional flow of factors, the combination of macro and micro data, and the lower level of indicator construction. At the same time, we attempt to provide theoretical research references for policy formulation to improve the coordination and integration of urban and rural development in China in the new era, and to achieve the goal of common prosperity.
Based on the traditional financial indicators, this paper applies text analysis and natural semantic processing methods to reconstruct the enterprise risk identification index system based on past and future perspectives. Then, it introduces machine learning methods to construct an enterprise risk identification model based on the financial data of listed companies and the textual information of management discussion and analysis as the data source for enterprise risk identification and prediction. The conclusions of the study are as follows: 1) By providing additional information, the risk measurement scale can be improved, and a three-dimensional risk identification system that combines temporal sensitivity and emotional insight can more comprehensively and accurately measure and identify business risks. 2) Introduces machine learning algorithms to compare the predictive accuracy of the AdaBoost model, Hist Gradient Boosting model, Random Forest model and Bagging model, and finds that the AdaBoost model is optimal, has the best robustness, and can be used for enterprise risk identification and prediction. 3) By applying machine learning and SHAP methods to rank the importance of enterprise risk characteristics and analyze the mechanism of enterprise risk identification, the key influencing factors of enterprise risk can be identified, and the impact mechanism of various risk characteristics on the enterprise risk identification model can be observed. This study can provide empirical evidence and decision support for the design of enterprise risk identification index system and optimization of risk identification model, as well as promote the high-quality development of enterprises and supply chain security and stability.
Corporate technological innovation cannot be separated from continuous organizational learning. Based on organizational learning theory, this article examines the impact and mechanism of corporate venture capital on technological innovation. Using manually verified data from 647 Chinese A-share listed companies participating in corporate venture capital activities from 2010 to 2022 as a sample, empirical testing found that: ① CVC has a significant promoting effect on corporate technological innovation. ② CVC mainly promotes technological innovation in enterprises by driving digital transformation and enhancing their absorptive capacity. ③ The industry distance and transportation time between the parent enterprise and the start-up enterprise have a negative moderating effect on the relationship between CVC and enterprise technological innovation, while the impact of spatial distance is not significant. The impact of CVC on enterprise technological innovation has regional heterogeneity characteristics. ④ Compared with the western region, CVC has a better promoting effect on enterprise technological innovation in the eastern and central regions with better economic development. The research conclusion of this article provides decision-making basis and empirical evidence for further utilizing CVC to promote technological innovation in enterprises.
