A-share Market Research Articles

Study of effectiveness of the ARIMA method in forecasting stock process in China

The stock market is volatile, and the prices of stocks are often influenced by various factors that enhance the complexity of stock prediction. According to the literature review, The ARIMA (autoregression average integrated moving average) model is one of the most-used methods for financial prediction, the effectiveness of which has been tested in many countries, which also leads to a need for accurate examination of the model for Chinas A-share market. In the paper, Due to its suitability for short-term forecasting, the ARIMA model is utilized to forecast the prices of three representative A-share stocks over 24 days. Finally, the efficiency of the short-term prediction and the low accuracy of the long-term prediction of the ARIMA model are primarily confirmed, which is worthy of further study.

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.

Related party M&A, goodwill impairment and stock price crash risk: Evidence from Chinese capital market

This article takes A-share market M&A cases from 2010 to 2022 as samples to investigate the influence mechanism of related M&A, goodwill impairment and the risk of the stock price crash, and the differences of the above effects in different industries. The study finds that related M&A presents a higher premium rate in the asset-light industry, which deepens the risk of goodwill impairment and brings a greater stock price crash risk. Further research shows that frequent related M&A behavior deepen the positive correlation between goodwill impairment and stock price crash risk. The enhancement effect is pronounced in the asset-light industry. This paper deepens the comprehensive understanding of related M&A and enriches the related research on goodwill impairment, which makes it specific reference value to small and medium investors.

The lower the integrity, the higher the risk: how does the lack of corporate integrity exacerbate risk?

Corporate integrity is not only a core element of its success but also plays a crucial role in the development of the corporation itself, consumers, investors, and society as a whole. This paper focuses on companies listed on the A-share market from 2017 to 2022, delving into the impact of corporate integrity on business risk. The findings indicate a significant negative correlation between corporate integrity and business risk, primarily mediated through the profitability of the corporation. Further heterogeneous analysis reveals that for large and state-owned enterprises, the impact of corporate integrity on their risk levels is particularly pronounced.

Informed Trading and Return Predictability in China: Research Based on Ensemble Neural Network

ABSTRACT We construct a new informed trading index based on the high-frequency trading data of the Chinese A-share market using the ensemble neural network algorithm. We find that the informed trading index is a strong negative predictor of future aggregate stock market returns, with monthly in-sample and out-of-sample R 2 of 5.45% and 3.53%, respectively, which is far greater than the predictive power of other previously studied informed trading indicators and macroeconomic variables. The asset allocation strategy based on our index can generate large economic gains for the mean-variance investors, with annualized CER (certain equivalent return) gains ranging from 10.91% to 7.80% as the investor’s risk appetite varies. The driving force of the predictive power appears to stem from the liquidity provider role that informed traders play, which decreases the market’s illiquidity risk and lowers the risk premium of equity. Our analysis complements the returns predictability study by adding a new predictor on the one hand and informs market timing strategies on the other.

Application of Multi-Factor Financial Models in Asset Pricing

This paper explores the theoretical underpinnings and practical applications of multi-factor models in stock portfolio management. It outlines the theoretical frameworks of multi-factor asset pricing models such as APT theory, Fama-French three-factor, and five-factor models, analyzing how various risk factors influence asset returns. The study then discusses methods for identifying core risk factors empirically, constructing portfolios, optimizing weight allocations, and evaluating investment performance. Empirical testing using data from the CSI 300 and CSI 800 indices confirms that optimized multi-factor portfolios achieve significant excess returns while managing risk effectively, showcasing the practical utility of this quantitative investment framework in the Chinese A-share market.

Enterprise Ownership, Corporate Governance, and Enterprise Innovation: Empirical Evidence from Listed Companies in Shanghai and Shenzhen A-Share Markets

Enterprise Ownership, Corporate Governance, and Enterprise Innovation: Empirical Evidence from Listed Companies in Shanghai and Shenzhen A-Share Markets

Excess return and tracking errors of Chinese ETFs

We adopt 72 passive equity exchange-traded funds (ETFs) from the Chinese A-share market to examine the size and time-varying characteristics of their tracking errors, identify their determinants, and gain insight into the persistent and positive excess returns. Net asset value traces index better than closing price does; surviving ETFs perform better than delisted ones. The common determinants are fund expenses, liquidity and volatility of the index. Many determinants asymmetrically influence the tracking errors in bull and bear markets. Further examination based on the capital asset pricing model indicates persistent positive excess return is a result of A-share market inefficiency.

Impact of Fintech on labor allocation efficiency in firms: Empirical evidence from China

Fintech has significantly influenced the traditional financial industry by introducing advanced technologies and innovative business models that have resulted in profound impacts. This study examines the effects of Fintech development on labor allocation efficiency and explores its underlying mechanisms. Using a set of Chinese A-share public firms from 2011 to 2020, we find that Fintech development plays a positive role in labor allocation efficiency, mainly by mitigating labor overinvestment. This positive effect is further reinforced by market competition. We also find that the primary pathways of this enhancement include lowering information asymmetry, mitigating agency issues, and substituting low-skilled labor. Moreover, we show that the dimensions of depth and digital integration are particularly important in improving labor allocation efficiency.

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.

The Asymmetric Effect of Mutual Fund Herding on Price Deviation: Evidence from the Chinese Stock Market

Mutual funds, as significant participants in financial markets, play a crucial role in the formation of asset pricing. The relationship between herding behavior of mutual funds and mispricing has been a subject of intense debate among scholars. In this paper, we investigate the asymmetric impact of mutual fund herding on price deviation and explore the underlying mechanism. In terms of research design, this study selects relevant data on mutual fund holdings and stock trading in the Chinese A-share market from 2010 to 2023, we examine whether herding behavior leads to mispricing, specifically focusing on the asymmetric effects of herding buying and herding selling on stock price deviation from intrinsic value. This study distinguishes the trading direction of mutual fund herding behavior and separately examines the relationship between herding buying behavior and herding selling behavior with stock price deviation. Our empirical findings reveal that buying herding behavior is linked to positive price deviations from intrinsic value. However, selling herding behavior shows no significant relationship with price deviation. Furthermore, our results provide evidence that the herding buying of mutual funds stimulates similar trading sentiments among other market participants and contributes to the formation of market bubbles, while herding selling prompts counterbalancing trades, mitigating the impact of price deviation.

Does digital finance drive the green level of transportation companies? Coordination effects of governmental digital preference

This paper examines the influence of digital finance (DF) on the green level of transportation companies (GLTC, n = 112) listed on the Chinese A-share market from 2011 to 2021 through moderation and mediation models. According to the conclusions, first, a significantly positive correlation exists between DF and GLTC. Second, DF indirectly promotes GLTC by enhancing corporate value and alleviating financing constraints. Both enterprise value and financing constraints have a chain mediation effect. Third, the government's digital preference can enhance the role of DF in promoting GLTC and alleviating financing constraints. In addition, governmental digital preference can moderate the impact of financing constraints on GLTC. Fourth, the effect of DF on GLTC exhibits regional, scale, and ownership heterogeneities. Drawing on the conclusions of the empirical analysis, several pertinent recommendations are proposed from micro and macro perspectives.

Environmental regulation and enterprise behavior in China: rent-seeking or innovation?

While the Porter hypothesis posits that well-functioning markets and government regulations can promote enterprise innovation, it remains less clear in developing and transition economies. To address this gap, we utilize enterprise data from China’s A-share market between 2005 and 2017 to empirically examine whether implementing more stringent environmental regulation will prompt regulated enterprises to engage in rent-seeking or innovative behavior. Our findings reveal that regulated enterprises increase both their rent-seeking and innovation efforts in response to environmental regulation. Importantly, enterprises exhibiting lower levels of rent-seeking demonstrate more substantial increases in rent-seeking expenditure when confronting environmental regulation. Furthermore, we discover that state-owned enterprises are more inclined to implement innovative behaviors, while foreign enterprises significantly reduce their innovative activities. This study highlights the complex relationship between environmental regulation and enterprise behavior in developing or transition economies.

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.

Can the Water Resource Fee-to-Tax Reform Promote the “Three-Wheel Drive” of Corporate Green Energy-Saving Innovations? Quasi-Natural Experimental Evidence from China

The long-standing, unrestrained utilization of energy resources by China’s manufacturing sector has created irreversible obstacles to regional sustainable development. Consequently, the Chinese government has implemented a water resource tax policy in certain regions, with the aim of compelling manufacturing enterprises to adopt green and energy-saving innovations. This study used panel data from Chinese manufacturing companies listed on the A-share market from 2009 to 2020 and employed a double machine learning model to explore whether the water resource fee-to-tax reform can compel enterprises to enhance their tripartite green energy-saving innovation drive. These innovations consist of vision-driven and mission-driven green energy-saving technological innovations and green management energy-saving innovations. Following a quasi-natural experiment, our findings revealed the following: (1) The water resource fee-to-tax policy promoted the internal coupling coordination of the triple-driven system. (2) The policy compelled progress in mission-driven green energy-saving technological innovations and green energy-saving management innovations but hindered vision-driven green energy-saving technological innovations. (3) Within the internal systems of manufacturing enterprises, green energy-saving management innovations play a positive mediating role between the water resource fee-to-tax policy and the mission-driven green energy-saving technology innovation subsystem, but they lack a similar positive mediating mechanism for the vision-driven green energy-saving technology innovation subsystem. (4) The counterfactual framework verified that the mechanistic pathway “water resource fee-to-tax → green energy-saving management innovation → mission-driven/vision-driven green energy-saving technological innovation” could be further extended to other manufacturing enterprises not currently under policy compulsion. (5) In the interaction system between manufacturing enterprises and external markets, the development of marketization and financial technology positively regulated the promoting effect of the water resource fee-to-tax policy on mission-driven green energy-saving technological innovations and green energy-saving management innovations, but it did not have a similar effect on vision-driven green energy-saving technological innovations.

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.

Corporate financialization and green technological innovation: Evidence from China

Corporate financialization and changes in the external financial environment have important but unexplored implications for firms' green technological innovation (GTI). Taking a sample of non-financial listed companies in China's A-share market from 2010 to 2020, and using panel and mediation models, this paper investigates the role of corporate financialization on GTI. We reveal that: (1) corporate financialization notably dampens GTI, especially substantive GTI. (2) The “crowding-out” effect proved the main mechanism by which corporate financialization affects GTI and there was no “reservoir” effect during the sample period. (3) Corporate financialization has a larger dark impact on GTI when firms with higher environmental, social, and governance scores and levels of industrial innovation, lower levels of digital financial development and industrial knowledge spillovers, and when firms are younger or non-state owned.

Green technology innovation and carbon emission efficiency: The moderating role of environmental uncertainty

Given the significant global warming caused by large-scale carbon emissions, it has become a crucial issue affecting human survival and world development. As a supportive means for relevant policies, the implementation of green technology innovation is essential for effectively achieving dual carbon goals. In order to explore the intrinsic relationship and impact mechanism between green technology innovation and carbon emission efficiency, this study takes data from high-energy-consuming manufacturing companies listed on the A-share market in China from 2010 to 2019 as samples. It uses the Super-SBM model to measure the carbon emission efficiency of sample companies and employs a two-way fixed effects model to verify the impact of green technology innovation on the carbon emission efficiency of high-energy-consuming manufacturing enterprises. Furthermore, this study also explores the external mechanism of green technology innovation related to carbon emission efficiency, focusing on the moderating variable of environmental uncertainty. The study found that both the quantity and quality of green technology innovation can significantly promote the carbon emission efficiency of high-energy-consuming manufacturing enterprises, and the accuracy of the results remains unchanged after conducting robustness tests. Environmental uncertainty plays an important role in moderating the process of green technology innovation, affecting the carbon emission efficiency of high-energy-consuming manufacturing enterprises. Heterogeneity tests show that the impact of green technology innovation on carbon emission efficiency is particularly significant in the central region and in enterprises experiencing decline. The policy implications derived from empirical analysis aim to provide empirical evidence for promoting the high-quality development of China's high-energy-consuming manufacturing enterprises.