China A-share Market Research Articles

Can digital transformation promote hospitality and tourism firms’ investment? Moderating effect of financing constraints and economic policy uncertainty

ABSTRACT Research on corporate investment is widely acknowledged as essential in the realm of financial management within the hospitality and tourism sector. Drawing on information asymmetry and resource dependency theories, this study examines the effect of digital transformation on hospitality and tourism firm investment and explores how financing constraints and economic policy uncertainty moderate these effects. We utilized firms engaged in tourism, hotel, catering, and transportation, listed on the A-share market of China’s Shanghai and Shenzhen stock exchanges from 2009 to 2022, as our research sample. Our findings indicate that digital transformation enhances investment in hospitality and tourism companies. Furthermore, this positive effect was more pronounced for firms with reduced financing constraints and heightened economic policy uncertainty. These findings reveal ignored tourism and hospitality investment antecedents and enrich academic understanding by expanding firm-specific boundaries. This study provides practical insight into how hospitality and tourism firms can boost investment through digital transformation.

Research and Analysis of Connected Transactions and Firms' Listing Denials

Abstract: In recent years, IPO declarations of proposed listed companies in China's A-share market have been rejected more and more frequently due to the issue of connected transactions, which has aroused concerns. The purpose of this paper is to explore in depth the reasons for the rejection of the A-share listing of proposed listed companies and to propose countermeasures to solve the problem of abnormal connected transactions. This article first defines the scope of connected transactions and their problems, analyzes some cases of proposed listed companies being rejected due to connected transactions in recent years, and deeply explores the problems of hidden connected relationships, connected transaction fraud, and connected transaction de-connectedness, etc., and demonstrates the specific manifestations and impacts of these problems through example analysis. Given the findings, the article puts forward a series of recommendations and insights, including maintaining the operational independence of enterprises, improving the sustainable profitability of enterprises, and tightly controlling the truthfulness of corporate information disclosure. These suggestions aim to help enterprises avoid the problems of connected transactions, enhance the success rate of IPO, and promote the healthy development of the capital market.

Unlocking the Reversal Anomaly in the A-share Market: Insights from Marginal Funds of Institutional Investors

Fama's Efficient Market Hypothesis (1970) posits that all valuable information is swiftly reflected in stock prices in mature markets. However, anomalies like momentum and reversal effects persist in China's A-share market. This study investigated the short-term reversal effect using turnover rates and institutional investor marginal funds. Significant reversals were found in high-turnover stocks due to speculative trading and deviations from intrinsic value. Institutional investors played a crucial role in correcting mispricing. The Institutional Holding Change Ratio (IHCR) demonstrated that institutional investors' marginal funds strongly explain the reversal anomaly and act as a style factor with positive returns. This research highlights the impact of institutional and individual investor behaviors on market efficiency and investment strategies.

An Empirical Study on Asset Pricing Models Based on The Chinese A-Share Market

Based on the analysis of the A-share markets in Shanghai and Shenzhen Stock Exchange from 2000 to 2023, this paper systematically evaluates the applicability and explanatory power of the Capital Asset Pricing Model (CAPM), the Fama-French Three-Factor Model (FF3) and the Fama-French Five-Factor Model (FF5) in the Chinese market. The study finds that although CAPM has some explanatory power in the early stage, the three-factor and five-factor models can better explain the cross-sectional changes in stock returns as the market develops. In the descriptive statistics, each factor's volatility and correlation reveal the Chinese market's unique characteristics, such as the relative strength of small-cap and growth stocks. In the regression analysis, the results of the Fama-MacBeth regression showed that the significance and explanatory power of the market factor, size factor and value factor were different in different models. Further robustness analysis reveals the COVID-19's impact on the model's performance through the sub-period analysis. This paper provides a new perspective on understanding the asset pricing of China's A-share market and a theoretical basis for investors' investment decisions. It also points out the limitations of existing models in explaining emerging market equity returns. It suggests that future research can deeply explore the performance of more factors in different market environments.

A Study of Stock Returns in China's A-share Market Based on the Fama-French Three-factor Model

In recent years, the world's financial boom, all kinds of financial products are emerging, how to rationalize the pricing of financial assets, is a new test of asset pricing theory. The capital asset pricing model of the 1970s, although set up rigorously, many studies have shown that it is unable to fully explain the stock returns. In view of this, academics began to explore other variables affecting stock returns and gradually focused on multifactor models. In this regard, the Fama-French three-factor model stands out. In addition to focusing on the impact of the market premium factor on stock returns, it also simultaneously takes into account that the market capitalization factor and the book-to-market ratio factor also have a significant impact on stock returns. Although the explanatory power of the Fama-French three-factor model on stock returns has been verified in major developed capital markets abroad. However, when applying the three-factor model to the Chinese market, the views of various scholars are different. This paper examines the data of SSE A-shares for the past 10 years and tests the adaptability of the three-factor model in the Chinese market with monthly data, and finds that the market premium factor and the market capitalization factor are significantly effective in explaining stock returns, in contrast, the book-to-market ratio factor is relatively weak in its ability to explain stock returns.

Environmental regulation, academic top managers and green innovation: Evidence from China

This study examines the impact of top managers’ academic backgrounds on green innovation within the context of China’s new Environmental Protection Law. Utilizing green patent data from listed companies in the China A-Share market from 2008 to 2019, and employing the DID approach, we find the law significantly boosts green innovation in firms led by academically skilled managers. However, this effect diminishes in state-owned enterprises and financially constrained firms. Our findings emphasize the critical roles of managerial academic backgrounds and autonomy in advancing green innovation.

The impact of digital finance on firm's inefficient investment: Evidence from Chinese A-share listed companies

Using data from China's A-share market, this study identifies that regional digital finance development significantly increases corporate investment inefficiency. This is because digital finance increases enterprises’ access to finance by reducing financing constraints. Digital finance development materially exacerbates corporate investment inefficiency by loosening financial regulations. The deterioration is not significant amid tightening financial regulations. While deterioration is significantly positive in non-state-owned enterprises (non-SOEs), it is not significant in state-owned enterprises (SOEs). The findings suggest that the government and firms must consider consequent investment risks amid digital finance development.

Only strong short-term contrarian effect exists in Chinese stock market: The role of the T+1 trading mechanism

We use an alternative sorting method which groups stocks into return intervals rather than percentiles to test the relationship between the T+1 trading mechanism and the short-term contrarian effect in the China's A-share market. Our sample period is from January 2010 to December 2020. China's stock market exhibits a significant contrarian effect in daily, weekly, and monthly frequencies, while the momentum effect is not significant. This paper argues that the special T+1 trading mechanism of China's stock market is the reason for this typical anomaly. The essence of the T+1 trading mechanism is a marketability put option, and this paper takes the volatility as the core of the analysis. This study shows that stock market volatility has a significant impact on the overnight return, with higher volatility leading to a higher discount. Through different daily, weekly, and monthly frequency groupings, the study finds that the winner's portfolio would reverse significantly in the next period. Under the T+1 mechanism, the high return stock portfolio is also characterized by high volatility and high turnover. The contrarian effect of China's stock market is unique in that both the winner's portfolio and the loser's portfolio will fall, but the winner's portfolio will fall more. The reversal of the winner's portfolio dominates in the entire market. The paper provides a new explanation for the contrarian effect and adds a unique explanation to the study of worldwide contrarian effect.

New quality productivity and environmental innovation: The hostile moderating roles of managerial empowerment and board centralization

As new quality productivity (NQP) emerges as a rising star of productivity that can effectively leverage technological innovation and sustainability, this study aims to explore the relationship between NQP and environmental innovation, with a particular focus on the roles of managerial empowerment and board centralization within the context of China A-share listed companies. Utilizing the entire sample of China A-share market from 2013 to 2022, the study analyses the effectiveness of various dimensions reflecting innovation engagement among Chinese listed companies. For measuring NQP, the entropy method is employed to calculate the weights. By controlling for industry and year effects, the study examines both the main and moderating effects of managerial empowerment and board centralization. Additionally, heterogeneity tests, robustness checks, and two-stage least squares (2SLS) estimation were conducted to address endogeneity concerns. The results demonstrate that NQP significantly enhances environmental innovation, with managerial empowerment supporting this positive effect and board centralization obstructing it. The positive effect of NQP is particularly evident in state-owned enterprises, while in heavily polluting industries, the anticipated positive moderating effect of top managers disappears due to strict regulatory environments. Furthermore, board centralization negatively moderates environmental innovation, especially in lightly polluting sectors where internal governance is more sensitive. The study underscores the importance for policymakers to tailor regulations that balance managerial empowerment and board centralization to enhance the transformation of NQP into environmental innovation. Future research is needed to further investigate different background settings and the mechanisms through which NQP influences sustainability.

Decoding the influence of servitization on green transformation in manufacturing firms: The moderating effect of artificial intelligence

This research addresses three crucial dimensions in operations management: the servitization of manufacturing, the utilization of artificial intelligence (AI) platforms, and green transformation. Employing the by-production method, we construct a metric for green transformation applicable to listed firms. Our comprehensive analytical framework integrates the resource-based view and information asymmetry theories, enabling systematic investigation into the influence of manufacturing servitization on firms' green transformation. In addition, we examine the moderating effect of AI platforms on the execution of servitization strategies. The empirical foundation of our study is an annually updated dataset of 554 manufacturing firms listed on China's A-share market. Our findings reveal a strong positive correlation between the deployment of servitization strategies and the green transformation of firms. This association withstands multiple robustness tests, including core variable substitution, outlier removal, and adjustments in clustering standard errors. Our research uncovers notable nuances. The effect of servitization on green total factor productivity is more visible for eastern and central China firms. Also, state-owned enterprises demonstrate a more conspicuous influence from servitization strategies. However, we observe a slight diminishing of this effect in firms audited by the Big Four. An essential contribution of our study is the illumination of the role AI platforms play in enhancing the efficacy of servitization. These AI platforms facilitate the creation of tailored solutions that curtail resource wastage, thus amplifying the positive effect of servitization strategies on green transformation.

China's cognitive bias premium: An exploration of innovation information

The complexity of innovation information often results in investors' cognitive bias, manifesting as an imbalance in the significance attributed to innovation quantity versus innovation quality. Based on investor cognitive bias, this paper explores the problem of innovation pricing in China's A-share market. Empirical findings indicate that cognitive bias creates a premium, primarily driven by mispricing. Moreover, we contend that mispricing originates from the innovation information environment and firms' innovation strategy. Our heterogeneity analysis indicates that the cognitive bias premium is more pronounced in high-tech industries, firms with lower market capitalization, and those facing stronger financial constraints.

Impact of supply chain finance on the business risk of core enterprises: evidence from China

PurposeThis study aims to explore the relationship between supply chain financing (SCF) and the business risks of core enterprises, the economic value of SCF for core enterprises and the motivation for core enterprises to participate in SCF. The authors also examine the mediating effects of financing constraints.Design/methodology/approachThis study analyzes the panel data of 393 companies listed on the main board of the A-share market in China from 2011 to 2014 using fixed-effect and intermediary-effect models.FindingsThe development of SCF in core enterprises can significantly reduce business risk by alleviating financing constraints.Research limitations/implicationsThe study sample is from China’s A-share market, which may limit the ability to generalize results. The indicators used to measure SCF primarily consider commercial credit, which may have affected the accuracy of the study.Practical implicationsThis study provides a new basis for core enterprise managers in the manufacturing sector to conduct SCF and control business risks. SCF with small and medium-sized upstream and downstream enterprises can reduce business risks and enhance competitiveness, especially under financing constraints.Originality/valueThis study focuses on core enterprises, a topic less explored in academia. It examines the impact of SCF on their performance and the mediating role of financing constraints. This study offers a novel perspective on the SCF transmission mechanism of supply chain finance and provides new insights for core enterprises.

An Empirical Study on the Impact of Investor Gambling Factors on Stock Cross-Sectional Returns

This study explores how investors' gambling preferences affect the cross-sectional returns of stocks in China's A-share market through behavioral finance factors. The portfolio ranking method and Fama-Macbeth regression analysis show how gambling behavior affects stock returns. On this basis, a multi-factor model (CHG model) incorporating gambling factor innovation was constructed to capture market dynamics more accurately. Research shows that going long on a low gaming index stock portfolio and short on a high gaming index stock portfolio can achieve significant average monthly returns. After adjustment by the Fama-French three-factor model, the returns of this long and short portfolio are significant at the 1% level. Significantly. In the CHG model, the coefficient of the gambling index Gamble is -0.0241, and the t-statistic is -6.1858, which is significant at the 1% level, further confirming the negative impact of gambling preferences on stock returns. In addition, the CHG model's ability to explain anomalies is also better than the CH-4 model. This study provides a new perspective on understanding behavioral finance factors in the Chinese stock market, and the results highlight the importance of considering behavioral finance factors in asset pricing and investment decisions in a highly irrational market environment. Future research can further explore the performance of behavioral financial factors in different market environments, as well as the differences and similarities of behavioral factors in different markets.

Research on the Problems of the A-share Market in China and Analysis of Countermeasures

Research on the Problems of the A-share Market in China and Analysis of Countermeasures

Research on Stock Selection Strategies Based on the Basic Value of Stocks Based on Buffett's Theory

The paper presented is highly significant as it explores the applicability and effectiveness of Warren Buffett's stock value investing theory in China's A-share market. By employing five key indicatorsdividend yield, debt-to-asset ratio, ROE, net profit growth rate, and the 200-day moving average (MA200), the paper attempts to adjust the values of these indicators to construct a model. This model is tested on the Chinese A-share market to verify its universality and effectiveness. Ultimately, the author concludes that the stock value investing theory is practical in the Chinese market, although the numerical values differ significantly from those recommended in traditional Western literature. Specifically, the return on equity (ROE) within a specific parameter range yields better returns, which are a quarter lower than the values mentioned by Buffett; the forecasted net profit growth rate for the coming year shows a slight negative correlation with excess returns within a certain range; and the latest balance sheet debt-to-asset ratio and dividend yield are positively correlated with excess returns within a specific range.

Does artificial intelligence reduce corporate energy consumption? New evidence from China

Artificial intelligence is playing a significant role in addressing the energy crisis. This study selected data from manufacturing companies listed on China's A-share market from 2011 to 2022 and calculated the total energy consumption for the first time. The data include the usage of coal, natural gas, gasoline, diesel and water consumption, electricity usage, and centralized heating. The data were then matched and merged with robot usage data from the International Federation of Robotics to empirically study the impact and mechanism of artificial intelligence on energy consumption levels. Our findings reveal that energy consumption decreases by 0.20 % with a one-unit increase in artificial intelligence applications by a corporation, indicating artificial intelligence can significantly reduce energy consumption. The mechanisms by which artificial intelligence affects energy consumption include technological innovation and digital transformation. Additionally, a heterogeneity analysis revealed that applying artificial intelligence in state-owned enterprises, high-tech companies, and non-heavy-pollution industries can further reduce energy consumption. Our study also provides important practical implications for formulating and optimizing global energy policies to achieve sustainable development goals.

Insider opportunistic trading through fast sales: Evidence from China

Using a sample of insider sales from firms listed on China's A-share market from 2010 to 2023, we provide robust evidence that insiders engage in fast sales when stocks have better pre-sale performance, compared to slow sales. Additionally, these fast sales predict lower abnormal returns in both the short and long term. Insider opportunistic trading through fast sales is more prominent in firms with high levels of information asymmetry and among insiders facing intense information competition. We find weak evidence that independent directors, institutional investors, and analyst coverage play a role in reducing such insider opportunistic trading. Finally, insider opportunistic trading through fast sales also predicts subsequent stock price crashes and extremely negative earnings news. Our findings help decipher the information content of insider sales in China's market.

The impact of climate policy uncertainty on corporate pollution Emissions——Evidence from China

Climate change is considered one of the major systemic risks facing the world in the 21st century. To address climate change, China has adopted a series of climate policies, but the uncertainty brought about by frequent climate policy issuance has increased pressure on enterprises, which may not be conducive to enterprises reducing emissions. This paper uses data on 1211 listed companies on the A-share market in China from 2012 to 2022 to study the impact of climate policy uncertainty on enterprise pollutant emissions. The research findings show that climate policy uncertainty increases corporate pollution emissions; climate policy uncertainty mainly generates negative impacts on enterprise environmental regulation, social responsibility, and R&D investment, thereby negatively affecting enterprise emissions reduction. Further heterogeneity analysis shows that climate policy uncertainty in China has a more significant impact on non-state-owned enterprises, technology-intensive enterprises, lightly polluting enterprises, and enterprises in western regions. These findings emphasize the importance of enterprise social responsibility, environmental regulation, and R&D investment in enterprise emissions reduction and provide policy implications for Chinese enterprises to optimize their energy-saving and emission reduction strategies in the face of climate policy uncertainty.

Corporate digital transformation and financing constraints: The moderating effect of institutional investors

In the age of digitization, digital transformation has emerged as a crucial pathway for companies to achieve sustainable development. In a sample of 24,103 firm-year observations from 3,508 listed companies in China's A-share market between 2007 and 2020, we investigate the relationship between corporate digital transformation and financing constraints based on dynamic capability theory. The proportion of intangible assets dedicated to digital technology is employed as a measure of the degree of digital transformation, and the SA index is utilized to assess the level of financing constraints. The findings demonstrate that digital transformation plays a significant role in alleviating financing constraints. In addition, greater institutional ownership leads to a pronounced negative correlation between digital transformation and financing constraints. From the perspective of dynamic capability theory, this study provides empirical evidence supporting research on economic consequences associated with digital transformation. Our results may contribute towards government formulation and implementation of policies promoting digital transformation to create a supportive external environment for businesses. Companies must seize opportunities associated with digital transformation to mitigate financing constraints.

Application of KMV Model in Credit Risk Management in Banking Industry

As an important part of the national financial system, the banking industry bears important responsibilities for the national economy. In addition, the banking industry is considered an area full of high risks. The risk management of banks not only involves the national finance, economy and national security, but also directly affects the national financial system and the stability of the global financial order. Credit risk has always been one of the risks that domestic and foreign banking banks pay special attention to. Effectively identifying and preventing credit risk is the key to ensure the smooth operation of banks. In order to better identify and manage the credit risk of the listed banks in China, this study uses the KMV model to measure the default distance of the sample banks. Will default distance as assessment of China listed commercial Banks credit risk agent variables, using before 2019 in China's a-share market listed commercial Banks build panel data, empirical analysis, and discuss the analysis results, put forward relevant policy Suggestions, model support for the healthy and steady development of the banking industry. This paper analyzes the current situation of the application of modern credit risk measurement model in the risk management of the banking industry. Meanwhile, combining with the data of some listed banks in China, discusses the credit risks and challenges faced by banks through in-depth analysis, and provides development suggestions with the data analysis of financial model.