China A-share Market Research Articles

The Test of CAPM and Fama-Franch Three-factor Model on China A-shares

William Sharpe et al. proposed the CAPM model in 1964, but subsequent empirical studies showed that the CAPM model still had limitations in practical application. In 1993, Fama and Franch proposed the three-factor model, which can explain the future return of the stock portfolio through the company size, the BM ratio of the companies, and express the excess return of a portfolio in the form of three factors. Based on the CAMP and Fama-French three-factor model, this study will use daily trading data of the constituents of the SSE 50 Index to analyze the A-share market by employing the market value factor, company size factor, and BM factor as explaining variables, so as to verify whether the CAPM and Fama-Franch three-factor model is suitable for China A-shares market. The final empirical results show that the CAPM model is applicable to China A-share market, but the validity test of the Fama-Franch three-factor model has not passed completely, that is to say, only in some industries, the Fama-Franch three-factors model can be effectively used.

Digital Transformation, Board Structure, and Organizational Innovation Capacity

In the context of rapid development in information technology, digital transformation has become an important factor affecting the innovation ability of organizations. This paper selects listed companies on China's A-share market from 2010 to 2022 as samples to study the relationship between digital transformation, board structure, and organizational innovation capacity. The findings demonstrate that digital transformation significantly enhances the organizational innovation capacity. This conclusion remains robust after several tests for robustness and endogeneity. Furthermore, this paper analyzes the moderating effect of board structure on digital transformation and organizational innovation ability. This paper enriches the empirical evidence on organizational innovation capacity and provides guidance for corporate governance.

Ripple effect of ESG sentiment: How news stirs the waves in China's A-share market

Ripple effect of ESG sentiment: How news stirs the waves in China's A-share market

The short-term predictability pockets in China

Previous studies by Farmer et al. (2023, 2024) identified local pockets of predictability in the U.S. market using a non-parametric method: the FST algorithm. We applied this algorithm to China's A-share market, which is characterized by a large proportion of retail investors and pronounced behavioral biases. To adapt to China's unique context, we incorporated three sentiment-driven variables while retaining the original ones. Our findings confirm the presence of predictability pockets in China, with IPO first-day returns and realized variance demonstrating significant predictive power. Although the original FST algorithm requires localized adjustments, we confirm the existence of these predictability pockets in China's stock market. Overall, our research indicates potential for practical trading strategies based on the FST algorithm in retail-dominated markets.

Navigating Complexity: GPT-4's Performance in Predicting Earnings and Stock Returns in China's A-Share Market

This study investigates the application of GPT-4, a large language model, in predicting earnings changes and stock returns within China's A-share market from 2000 to 2023. We evaluate the model's performance using various metrics, including prediction accuracy, F1 score, stock returns, Sharpe ratio, and alpha. Our findings reveal significant fluctuations in the model's predictive accuracy, ranging from 10.62% to 48.67%, with an average F1 score of 0.30. Despite inconsistent accuracy, the model maintained high prediction confidence levels between 75% and 90%. Stock returns associated with the model's predictions varied widely, from -4.86% to 13.59%, showing no consistent correlation with prediction accuracy. The study highlights the challenges of applying AI models to financial analysis in emerging markets, particularly given the unique characteristics of China's A-share market, such as frequent policy interventions and a high proportion of retail investors. We discuss the implications of these findings for the future of AI-driven financial analysis, emphasizing the need for improved model calibration, ethical considerations, and regulatory frameworks.

Green disguise or real action? The truth behind corporate greenwashing under stringent climate policy

Stringent climate policies have significantly impacted corporate operations and asset values, subjecting companies to increased pressure regarding environmental and social responsibilities, which may lead to the adoption of disingenuous greenwashing practices. Utilizing data from 1219 listed companies on China's A-share market from 2011 to 2022, this study investigates the relationship between stringent climate policies and corporate greenwashing behavior. The findings of this paper are as follows: First, there is a significant positive correlation between stringent climate policies and corporate greenwashing behavior, which has been validated through robustness tests; second, the impact of climate policy on corporate greenwashing exhibits heterogeneity. Enterprises located in the western and eastern regions, those with insufficient technological content, labor-intensive, and highly polluting companies have a greater incentive to engage in greenwashing activities; finally, this study confirms the transmission pathways of internal financial distress and external environmental subsidies as climate risks contributing to corporate greenwashing. This paper holds certain reference value for the government in establishing and improving mechanisms for identifying corporate greenwashing behavior, setting corporate conduct standards, and perfecting the corresponding regulatory system.

Investment efficiency of renewable energy enterprises when exposed to air pollution: Evidence from China

Previous research has focused on the impacts of air pollution on individual health and economic development, but has not considered its impacts on the investment efficiency of renewable energy enterprise (IERE). Therefore, this paper empirically analyzes the impact of air pollution on IERE using panel data for 200 non-hydroelectric renewable energy (RE) firms listed on China's A-share market from 2008 to 2021. This study shows that air pollution improves investment efficiency by mitigating under-investment in wind and photovoltaic enterprises. This effect is not significant in biomass energy enterprises. Investor attention and enterprise environmental engagement are the mechanisms by which air pollution improves IERE. In addition, the impact of air pollution on IERE is higher for state-owned enterprises, companies with environmentally background executives, areas with less market segmentation, and areas with higher levels of environmental justice. RE policies and media attention can increase the positive impact of air pollution on IERE. This study clarifies the relationship between air pollution and IERE, and highlights important implications for improving the RE policy system and informing corporate investment decisions.

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.