China Stock Market Research Articles (Page 1)

The financial sector has experienced swift growth over recent years, leading to the escalating prominence of credit risk among publicly traded companies. Consequently, forecasting credit risk for these firms has emerged as a critical task for banks, regulatory bodies, and investors. Traditional models include the z-score, the logit (logistic regression model), the kernel-based virtual machine (KVM), and neural network approaches. Nevertheless, the outcomes from these methods have often fallen short of expectations. Three major challenges in previous works are feature selection, imbalanced classification, and hyperparameter optimization. This paper presents a method for credit risk prediction for listed companies that uses an off-policy proximal policy optimization (PPO) algorithm for feature selection and imbalanced classification. The off-policy PPO, a reinforcement learning (RL) approach, enhances sample efficiency by more effectively utilizing past experiences during policy updates. This approach improves feature selection and the management of imbalanced classification by optimizing data use, thereby enhancing model training outcomes. Moreover, we use the Bayesian optimization hyperband (BOHB) approach to refine the hyperparameters of the method. BOHB merges Bayesian optimization and Hyperband, significantly speeding up the optimization process. We assess our model using the China Stock Market and Accounting Research (CSMAR), MorningStar, KMV default, Give Me Some Credit (GMSC), and the University of California, Irvine Credit Card Default (UCICCD) datasets. Our experimental findings demonstrate the excellence of the model over existing state-of-the-art models, achieving F-measures of 90.763%, 86.358%, 87.047%, 90.576%, and 89.485% on these datasets. These findings validate the efficiency of the method in economic settings, signifying a major progression in systems for predicting credit risk and enhancing investigative approaches.

Purpose This study examines the complementarity of two public enforcement mechanisms, the random audit inspection conducted by the CSRC and the initial public offering (IPO) review process for audit firms’ clients by the stock exchanges. Design/methodology/approach Our initial sample includes all firms listed on the STAR market from its establishment in July 2019 through December 2022. We manually collect random audit inspection data from the CSRC ROs' official website and the issuance of comment letters during the IPO process from SSE’s official website. We obtain other data (e.g. firms’ post-IPO performance data) from the China Stock Market and Accounting Research database (CSMAR). Findings Utilizing a hand-collected dataset of random audit inspection and IPO review data, we find that IPO review intensity significantly increases for clients of inspected audit firms, especially when the audit firms are found to have issues during the random audit inspection. This effect is stronger when the reputations of the audit firms or underwriters are lower and external oversight is strengthened. Furthermore, clients of inspected auditors face more inquiries requiring auditors' independent opinions, longer reply intervals and IPO delays. Following random audit inspection, there is a marked tendency among IPO firms to update their prospectuses. Due to the deterrence effect, we find that random inspections influence IPO approval rates and post-IPO performance when audit firms are penalized. Practical implications Firstly, despite the improvement of audit quality after the random inspection, our findings show the causal effect of random audit inspection on IPO review intensity, which can serve as evidence of regulatory complementarity. Secondly, our paper demonstrates that regulators are concerned about auditor regulation and the consequent higher public pressure in the IPO review process, which further highlights the gatekeeper role audit firms should play in information disclosure quality and the importance of public opinion in the IPO review process. Originality/value Firstly, our paper contributes to the extensive literature on the IPO review process by identifying the audit regulation as a significant regulatory antecedent. This study is one of the few that directly examines how regulatory activities within regulatory agencies affect exchange-led listing reviews. Secondly, we diverge from prior studies by introducing the random audit inspection to identify the causal effect and examining the real economic consequences of securities regulation.

This essay aims to find the impacts of the cut in interest in the US on Chinese stocks, including the attention to the prediction price of A stock, and the difference in behaviour in different industries. The cut in interest rate is an important policy that the federal government could exert, which often has significant effects on the global economy, especially for newborn countries. First, by data analysis, this paper evaluates the fluctuation in the short term and the tendency in the long term. Then, this essay focuses on the reaction of different industries, including consumption, finance and technology. At last, this paper concludes the methods that could be utilized in the research, including traditional finance model, machine learning, and text analysis, which means to accurately assess the impacts of the quantities methods. This paper provides the theoretical and exact evidence for figuring out the influence of the cut on A Stock from the flowing ability, capital flowing, and markets emotions. The finding indicates that the cut not only affects the markets fluctuation, but also the expected price in a particular industries and the portfolio of investors.

Assessing systemic importance using multilayer dynamic networks: Evidence from China's stock market

Economic policy uncertainty, carbon risk and China's stock market

ABSTRACT Based on the innovation orientation (IO) framework, the research examines how AEs systematically shape strategic IO, including innovation culture, behaviour and environment, and reveal multiple paths through which AEs impact firm innovation. Using path-specific effects (PSEs) and a counterfactual framework, we further identify the causal sequence mediation among IO dimensions, quantifying AEs’ contributions to innovation through different paths and enhancing causal inference accuracy. We construct a panel dataset of Chinese A-share listed firms from 2010 to 2019 using data from China Stock Market and Accounting Research (CSMAR) database, China Research Data Service Platform (CNRDS) and firm annual reports. Results indicate that AEs influence both innovation quantity and quality through IO, with a causal sequence mediation. We further conduct heterogeneity analysis and employ dynamic panel models (DPM) to assess the short- and long-term effects of AEs. Findings offer insights for policymakers and managers in optimising innovation strategies and governance.

ABSTRACTUsing overnight volatility as the proxy for overnight information, this paper models future Chinese stock market realized range–based volatility (RRV) within a class of heterogeneous autoregressive models augmented by this proxy. We confirm the important role of overnight information in volatility forecasting models with strong evidence from in‐sample and out‐of‐sample analyses. Moreover, such forecasting improvement is considerable at the short‐term prediction horizon but weakens as the prediction horizon extends. We conduct numerous robust tests to strengthen our findings, with alternative rolling window lengths, alternative loss criteria, and alternative volatility estimators. We also provide evidence that our forecasting model incorporating overnight volatility performs extremely well in volatility forecasting during times of market turbulence.

SUMMARY This study investigates the implications of effort allocation in integrated audits on financial reporting quality. We examine whether increased-reliance on internal controls, defined as auditors allocating greater effort to the audit of internal controls and less effort to the financial statement audit, relative to expected levels of effort, negatively impacts financial reporting quality. Based on a sample of 9,094 firm-year observations from companies listed on China’s stock exchanges during 2012–2019, we find evidence that increased-reliance on internal controls is associated with lower financial reporting quality. However, client importance mitigates this negative effect. Our main findings are robust to controlling for endogeneity, excluding observations whose abnormal audit fees are close to zero, and using a sample with stable total audit fees over time. Overall, our findings suggest that disclosure of fees related to internal control audits and financial statement audits provides information relevant to assessing financial reporting quality. Data Availability: The data used in this paper are available from China Stock Market & Accounting Research, Taiwan Economic Journal, and public filings. JEL Classifications: D81; G38; M42; M48.

This study explores the impact of economic policy uncertainty on herd behaviour in the Chinese stock market. As economic policy uncertainty increases, market information becomes highly chaotic and complex, making reliable information scarce and challenging for investors to make independent decisions. Particularly in the Chinese stock market, where retail investors dominate and generally lack professional financial knowledge and deep market analysis skills, these investors are more likely to mimic the behaviours of other market participants. Using monthly data from January 2011 to December 2023, and employing panel regression for empirical analysis, this research aims to explore the specific effects and mechanisms of economic policy uncertainty on herd behaviour, addressing a gap in the existing literature regarding how economic policy uncertainty directly influences investor behaviour in terms of manner and extent. The study's findings indicate that economic policy uncertainty has a significant and varied impact on herding behaviour across different market segments. Specifically, economic policy uncertainty significantly promotes herding behaviour in the Science and Technology Innovation Board, while it inhibits herding behaviour in the Main Board. Economic policy uncertainty also inhibits herding behaviour in the ChiNext, but not as significantly as in the Main Board. This diversity in impact highlights the complex nature of economic policy uncertainty's influence on herd behaviour.

ABSTRACTFrom an industry perspective, we apply the quantile regression to investigate the impact of investor sentiment (IS) and China's/the US economic policy uncertainty (EPU) on Chinese stock market volatility. Considering the structural break of the stock market, we found that China's and the US EPU/IS and their interaction effects had a significant impact on China's stock market volatility at the market level. Moreover, there was an asymmetric dependence between China's and the US EPU/IS and stock market volatility, and the dependence structure was time‐varying. At the industry level, the impact of the EPU on industry stock market volatility was highly heterogeneous, and its significance mostly occurred in the upper and lower tails. China's and the US EPU/IS can exacerbate industry stock market volatility in bullish and bearish markets. In addition, China's and the US EPU/IS and their interaction effects are heterogeneous and asymmetric, and the effects change with the break point. Finally, the US EPU has a great impact on the industry stock market. However, its scope and degree of influence are gradually decreasing. Our findings shed new light on the relationship of the EPU, IS and stock market volatility in China.

This study investigates the momentum and reversal effects in China's real estate and pharmaceutical industries using monthly stock return data from 2004 to 2024. The results show very different market behaviors: the real estate industry shows a short-term reversal effect and a long-term reversal effect that is not remarkable for momentum or inversion effects, whereas the pharmaceutical industry shows a long- and short-term significant reversal effect and a medium-term inconspicuous momentum effect. This study uses monthly data to investigate the cyclical performance of different industries in China's stock market, focusing on the distinct behaviors of stocks in the pharmaceutical and real estate sectors regarding momentum and reversal effects, and provides possible influencing mechanisms. This paper offers new and interesting evidence from a micro perspective to observe the connection between momentum and reversal effects in China's stock market and industry cycles.

As one of the most influential emerging countries in the world, China's international influence and economic status are gradually strengthening globally. The Shanghai Composite Index is the core stock index of the Shanghai Stock Exchange, reflecting the overall performance of A-share and B-share stocks in the Shanghai market. This index serves as a key benchmark for China's stock market. In order to better analyze the stock market situation and explore the practicality and limitations of the ARIMA model, this paper selected the closing prices of the Shanghai Composite Index from January 2, 2020 to April 30, 2024 as the representative sample of the Chinese stock market, and based on the ARIMA model predicted the closing prices of the Shanghai Composite Index. The results show that due to the semi-strong efficiency of the Chinese market and the unpredictable short-term price trends caused by external events, the prediction accuracy of the ARIMA model will be affected by extreme fluctuations. This paper analyzes the effectiveness and limitations of the ARIMA model in prediction, providing a reference for investors on index prediction methods.

The development of the stock market has important practical significance for market investors and the stability of the financial market. This article explores the problems in risk management in the Chinese stock market through in-depth analysis of three typical cases: the financial fraud case of Kangmei Pharmaceutical, the illegal reduction case of Zhonghe Titanium Industry, and the equity pledge explosion case of Dongfang Shanshui. Utilizing case analysis and regulatory system review methods, it identifies the root causes and proposes targeted solutions. The study reveals significant deficiencies in corporate governance and information disclosure, frequent market manipulation and insider trading, and prominent equity pledge risks. The key to reducing risks lies in improving corporate governance and information disclosure, strengthening market regulation, and standardizing equity pledge business. The conclusion indicates that optimizing the regulatory framework and market mechanisms is of great significance for enhancing the stability and transparency of China's stock market, significantly strengthening market stability and transparency, promoting its long-term healthy development, and protecting investors' interests.

PurposeThis study aims to examine how the stock market of Brazil, Russia, India, China and South Africa (BRICS) is reacting against the fear and greed mood of US investors. For that purpose, the author used the CNN US Fear and Greed Index (F&G Index) as an independent variable to examine its impact on the stock prices of BRICS countries.Design/methodology/approachThe author has applied a combination of methods supporting each other to decipher the unfolded interrelation between the US stock market and the BRICS stocks, especially related to the current time scenario. First, the DCC GARCH model is estimated to examine volatility transmission and conditional correlation between the same. Wavelet coherence plots were also used to intercept interaction between the F&G index and BRICS stocks. Quantile-and-quantile regression are also applied to examine the asymmetrical causal impact of the US F&G index on the stock prices of BRICS countries.FindingsThe US F&G Index acts as a global sentiment driver, with varying degrees of influence on the stock of BRICS countries. Brazil, South Africa and India have significant dynamic associations with the US F&G Index, whereas China and Russia exhibit weaker or negligible coherence. Fear in the US market has a more pronounced and significant impact on the BRICS stock markets compared to greed, and this asymmetry underscores the vulnerability of BRICS economies to negative US market sentiment. These findings highlight the asymmetric and heterogeneous impact of US market sentiment on BRICS economies, with implications for portfolio diversification, risk management and policy strategies in emerging markets.Originality/valueThe F&G index comprises seven different aspects of the stock market and may provide robust information on how the overall behaviours of US investors affect BRICS stocks. A comprehensive indicator like the CNN US Fear and Greed Index has not been explored previously in the given context, as per the best knowledge of the author. Therefore, the author gets the motivation to examine the impact of the US Fear and Greed Index on the stock prices of BRICS countries.

The rapid development of artificial intelligence is reshaping the global financial competition pattern. Studying the impact of AI on China's stock market is of great theoretical and practical significance for improving market efficiency and optimizing regulatory mechanisms. However, China's stock market is facing the limitations and efficiency bottlenecks of traditional analytical methods, so there is an urgent need to explore new ways with the help of AI. Studying the impact of AI on China's stock market is of great theoretical and practical significance for improving market efficiency and optimizing regulatory mechanisms. Through empirical analysis, case comparison and international empirical studies, this paper systematically explores the application of AI in the fields of high-frequency trading, quantitative modeling and regulatory technology, and analyzes its comprehensive impact on China's stock market efficiency, structural anomalies and regulatory model. The paper concludes that AI significantly improves market information processing speed and trading efficiency, but exacerbates the deterioration of small-order liquidity and the risk of algorithmic convergence and that China's regulatory framework centered on "technologically controllable" is more adaptable than that of the U.S. and Europe, but needs to guard against algorithmic failures in extreme markets. This paper lays a theoretical foundation for the construction of a "technologically controllable regulation" system with Chinese characteristics, and provides policy guidance for balancing AI technology innovation with risk prevention and control.

With the complexity of the global economic environment, enterprises are facing an increasing risk of financial distress. However, traditional financial risk assessment methods rely on linear assumptions, which have significant limitations in dealing with complex, high-dimensional, and nonlinear financial data of modern enterprises, making it difficult to accurately identify potential financial distress. To solve this problem, the study proposes a corporate financial distress prediction model based on graph convolutional neural networks and dynamic time regularization. The model firstly transforms the corporate financial data into graph structure, and extracts the features of complex financial relationships through graph convolutional neural network, and at the same time combines with the dynamic time regularization method to enhance the adaptability to the dynamic change of time. The experimental data are obtained from the China Stock Market and Accounting Research Database, covering the quarterly financial data of listed companies during the period from 2013 to 2020, which contains 120 financially distressed companies and 1,929 normal operating companies. The experimental results show that the proposed model achieves an accuracy of 67.47% in predicting financial distress, a recall rate of 72.36%, an F1 value of 68.58%, and a misclassification rate of less than 4%, which are all superior to traditional methods such as K-nearest neighbor, support vector machine and convolutional neural network. In addition, combined with business intelligence technology, a visualized financial forecasting system is constructed, which enhances the interpretability of the model and provides intelligent financial decision support for enterprise managers. The research results can provide theoretical basis for enterprise managers to optimize financial structure, investors to assess enterprise health, and the government to formulate financial regulatory policies.

Abstract China is the world's largest energy consumer and accounts for nearly one-third of global CO₂ emissions. The Shanghai Stock Exchange is the fourth-largest stock exchange in the world in terms of market capitalization, while the banking sector has the largest share in China's financial system. Given the prominence of both the banking sector and the stock market in China, specific policy recommendations are essential to achieve desired environmental outcomes. Therefore, this study aims to investigate the impact of both bank- and market-based financial development and economic growth on the environment in China. Unlike previous studies that primarily rely on conventional environmental indicators such as CO₂ emissions and the ecological footprint, this research introduces the Load Capacity Factor (LCF) as an innovative approach to assessing ecological balance. The Load Capacity Curve Hypothesis is tested using data from 1990 to 2020, employing the ARDL bounds test with structural breaks. The empirical findings indicate a positive and increasing connection between LCF and financial institutions as well as financial market indices in the short and long term. In contrast, the relationship between economic growth and LCF is negative and increasingly deteriorating, indicating the invalidity of the LCC Hypothesis in the context of China. Overall, the findings suggest that while financial development enhances environmental quality, economic growth exerts a detrimental effect. Based on these findings, the study emphasizes the need to promote green finance, expand investments in renewable energy, and strengthen cooperation between financial institutions, markets, and industrial sectors. Strengthening financial systems through legal and operational reforms, diversifying green financial instruments, and decreasing dependence on coal-powered energy generation are critical measures to achieve sustainable growth. Graphical Abstract

Foreign institutional investors and share pledging: Evidence from China's stock market openness reform

This study examines the impact of environmental risk factors on market pricing efficiency in China's stock market from 2018 to 2024. Using a comprehensive panel dataset of 2,486 listed companies, the research constructs a multidimensional environmental risk index incorporating both physical and transition risks. The empirical analysis reveals that environmental risks significantly impair market efficiency through direct operational impacts and indirect investor perception channels. A one-standard-deviation increase in environmental risk leads to a 0.186-standard-deviation decrease in price synchronicity and a 0.224-standard-deviation increase in price delay. The investor risk perception channel accounts for approximately 35% of the total effect. Cross-sectional analysis shows that environmental risk effects are 1.5 times stronger in high-pollution industries compared to low-pollution sectors. These relationships remain robust after addressing endogeneity concerns through instrumental variable estimation and various robustness tests. The findings contribute to the growing literature on environmental finance and have important implications for improving environmental risk disclosure frameworks and market efficiency in emerging economies.

In the research on the correlation between "risk and return", the capital pricing model and arbitrage pricing model have always occupied an important position, but in recent years, when the research market continues to expand and the research indicators are constantly enriched, the negative correlation between tail risk and cross-sectional returns of stocks has received more and more attention. Studies have used data from the U.S. stock market to find that the relationship between the left-tail risk of individual stocks and their cross-sectional returns is significantly negatively correlated, that is, stocks with higher left-tail risk have lower future returns. This finding contradicts the positive correlation between return and risk under traditional financial research, and then promotes the formation of left-tail risk anomalies. However, compared with the US stock market, the overall development time of China's stock market is relatively short, there are many retail investors, and market participants often do not have professional investment knowledge, so information asymmetry prompts blindly following market participants to invest irrationally like a herd, and most of the anomalies in asset pricing can be explained from the perspective of behavioral finance. Therefore, this paper draws on his research ideas to examine whether there is left-tailed momentum in the Chinese stock market, and whether the company characteristic indicator can strengthen this relationship and explain it through behavioral finance. This paper studies the period from January 1, 2000 to December 31, 2022, and aims to explore the cross-sectional correlation between left-tail risk and stock return in the coming month, using the return of all stocks in the Shanghai and Shenzhen markets to measure left-tail risk at risk. Firstly, the fundamental relationship between risk and return is explored through univariate combination analysis, and then the role and marginal contribution of company characteristic indicators on the relationship between risk and return are considered from the perspective of behavioral finance, considering common company characteristic indicators, such as book-to-market capitalization ratio, and indicators linked to poor investor information, such as analyst coverage, etc., to consider the role of corporate characteristic indicators on the relationship between risk and return from the perspective of behavioral finance. The results show that there is a significant left-tail momentum in China's stock market, that is, there is a negative correlation between left-tail risk and expected return, and the phenomenon of left-tail risk is more obvious in stocks with retail holdings and low analyst attention. When the yield is adjusted by the four-factor model, the performance of the left-tail momentum effect will be significantly strengthened, which is significantly better than the traditional capital asset pricing model and the three-factor model. In the research sense, frequent "black swan" events have made investors pay more and more attention to the huge risk contagion chain behind small probability events or extreme events, and at the same time suggest that the tail risks of relevant government departments may pose a major threat to the systematic smooth and orderly operation of the entire financial market.