Chinese Securities Market Research Articles

Economic Development and the Development of the Securities Market: A Study of China's CSI 300 Index

The Chinese securities market has experienced over two decades of development and has emerged as a significant global investment market. However, compared to other developed securities markets, it still faces issues such as an inadequate institutional framework, irrational investor behavior, and imperfect mechanisms. To explore the relationship between China's economic development and its securities market, this paper employs Eviews time series analysis and VAR modeling for empirical research, aiming to uncover the relationship and operational mechanisms between the two. The findings indicate a certain correlation between economic development and securities market development in China. The variance decomposition reveals that the contribution of economic development to the securities market is relatively low, highlighting a major issue within the Chinese securities market. Consequently, this paper proposes reform suggestions from various perspectives, including institutional, investor, and listed company aspects, to guide the healthy growth of the Chinese securities market and create a favorable environment for economic development.

Правовые аспекты доступа иностранных инвесторов на рынок ценных бумаг Китайской Народной Республики

The People’s Republic of China is the second largest economy in the world. One of the foundations of China’s economic prosperity is a well-developed financial market and, in particular, the securities market. However, the implementation of investments in the Chinese securities market by foreigners is significantly hampered by administrative barriers and direct legislative restrictions. This paper analyzes the legal regime of foreign investment in the securities market of the PRC. The legal regulation of separate legal mechanisms aimed at realization of direct foreign investments in the PRC securities market, such as the status of a qualified foreign institutional investor, as well as exchange and depository links, is researched. The comparative legal aspect assesses the correlation of foreign investment mechanisms in Russia and China and, taking into account the existing characteristics, proposes measures to develop mutually beneficial ways of legal regulation for both countries.

Financial market trend prediction model based on LSTM neural network algorithm

The financial market has randomness, and the prediction of the financial market is an important task in the financial market. In traditional financial market prediction models, the prediction results are often unsatisfactory. So it needs to introduce new models for financial analysis. To solve this problem, this paper analyzed a financial market trend prediction model based on LSTM (Long Short-Term Memory) NN (Neural Network) algorithm, and conducted an empirical analysis on the Shanghai stock index dataset. This paper first introduced the LSTM NN algorithm, and then divided it into training set, test set and comparison set according to the data characteristics. At last, this paper used the data preprocessing method to verify the LSTM NN algorithm. The experimental results showed that the LSTM NN algorithm analyzed in this paper can effectively improve the generalization ability of financial market trend prediction models while ensuring the prediction accuracy. Through experimental analysis, this paper found that the average accuracy rate of using LSTM NN algorithm was 2.25% higher than that of using traditional NN algorithm. This research is primarily aimed at developing effective methods for predicting stock market trends in the continuously evolving Chinese securities market. The core objective is to empower investors with precise guidance by enabling them to make well-informed investment decisions. Achieving accurate predictions holds the potential to significantly impact economic operations in a positive way. Therefore, this research direction is of paramount importance, offering substantial value both in academic exploration and practical application.

Financial distress prediction based on ensemble feature selection and improved stacking algorithm

PurposeWhile the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.Design/methodology/approachIn the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.FindingsAn empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.Originality/valueCompared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

Impacts of supply and demand shocks on abnormal fluctuation of stock price: An analysis of US–China trade friction

AbstractThis paper aims to study the impact of supply and demand shocks on abnormal fluctuation of stock price. We use event study and regression analysis methods to explain why the supply and demand shocks caused by US–China trade friction in targeting industries can affect the whole Chinese securities market. Our findings reveal a direct effect of shocks that can affect the stock prices of targeted companies. Additionally, there is a network effect in which the supply side and demand side shocks propagate upstream and downstream through the network, in turn affecting the stock prices of nontargeted companies.

Research on the Spillover Effects of Systemic Risk in China's Financial Market on The Securities Industry

In recent years, with the increasing openness of China's financial market, continuous deepening of financial reforms, and growing financial innovation, the Chinese securities market has become increasingly complex, leading to a gradual increase in the probability of systemic risk. This study focuses on the daily closing prices of stocks from eight large-scale listed bond companies, including CITIC Securities and GF Securities, and incorporates the stock, currency, bond, foreign exchange, and real estate markets into the CoVaR model to analyze the spillover effects and transmission channels of these five markets on systemic risk in the securities industry. The empirical results indicate that the stock market, currency market, bond market, and foreign exchange market have significant negative spillover effects on systemic risk in the securities industry, while the real estate market has a positive spillover effect. The empirical results also suggest that among the transmission channels of spillover effects, the currency market exhibits the strongest risk volatility, individual securities companies have the highest sensitivity to changes in the stock market risk, and compared to the real estate market, the rich information content of the four financial sub-markets has a greater impact on the spillover effects of systemic risk in the securities industry.

The Impact of Financial Distress on Corporate Performance in the Chinese Securities Market

This research aims to provide an in-depth investigation into the effects of financial distress on the operational and financial performance of companies. It employs a longitudinal study on a broad sample of listed corporations in the Chinese security market, analyzing their financial reports, stock performance, and various distress indicators over the past decade. The study also factors in macroeconomic conditions, regulatory changes, and industry-specific issues. The paper seeks to understand how financial distress influences firm performance, profitability, and survival, while also highlighting the strategies that distressed companies have employed to navigate these challenging circumstances. Furthermore, the research explores the role of governmental policies and corporate governance in mitigating the adverse effects of financial distress. This study will be particularly beneficial for stakeholders in the Chinese securities market, providing crucial insights for decision-making in financially turbulent times.

Signs of Stress: Identifying Indicators of Financial Distress in the Chinese Securities Market

This conceptual paper aims to explore the signs of stress and identify key indicators of financial distress within the Chinese securities market. With China's rapidly growing economy and evolving financial landscape, understanding the signals of potential market downturns and identifying early warning signs of financial distress is crucial for investors, policymakers, and market regulators. By analyzing a comprehensive range of financial and macroeconomic variables, this study aims to uncover robust indicators of financial distress specific to the Chinese securities market context. The findings of this research can inform the development of proactive risk management strategies and contribute to the overall stability and resilience of the Chinese securities market.

Effectiveness and Limitation of Markowitz Mean-variance Model: Evidence from Hang Seng Index

As China's economy has expanded rapidly, its financial system has gradually improved, new financial businesses have evolved, and new theories and technologies have also been developed. The Markowitz mean-variance model, the cornerstone of asset pricing theory, is utilized as an example in this paper to demonstrate how the Markowitz portfolio theory may be applied to assess a portfolio of five stocks in the Hang Seng data, a significant indicator of the Hong Kong stock market. Empirically, the optimal portfolios with the highest Sharpe ratios and lowest variances are identified. Their expected returns, standard deviations, and Sharpe ratios are then evaluated by comparing them to equal-weight portfolios and giving the effective frontier. The author further demonstrates the guiding significance of Markowitz's portfolio theory in the Chinese investment market through the assessment of the findings. Finally, in the context of the Chinese securities market, identify and analyze the model's constraints and offer suggestions.

Calendar Effect in The Chinese Securities Market

The existence of the calendar effect as a market anomaly problem proves that there are loopholes in the Efficient Market Hypothesis (EMH). By studying the calendar effect, scholars can gradually identify and solve the problems of regulatory loopholes and information asymmetry in the financial market. This paper divides the Chinese securities market into stock market, bond market, fund market and financial derivatives market, focusing on the weekday effect and the month effect. By summarizing and comparing the weekday effect and the month effect of the above four sub-markets, the paper shows that the calendar effect is more significant in all four markets on Mondays and Fridays, also January and December. The calendar effect of the four sub-markets is not similar in other periods, which may be due to the fact that each market is affected by uniform regulation and the same policies, but also has its own unique characteristics. The purpose of this paper is to summarize the research results on the calendar effects in the four sub-markets of the Chinese securities market and to promote the development and maturity of the Chinese securities market.

Can Fintech Alleviate the Financing Constraints of Enterprises?—Evidence from the Chinese Securities Market

Whether Fintech enabled by big data technology can improve the efficiency of credit allocation and how it would do has always been the focus in the capital market, especially the intermediary mechanism, which has not yet been convincingly explained. This paper empirically tests the logical relationship and micro mechanism between Fintech and the corporate financing constraint dilemma by using the data of China’s A-share non-financial listed companies from 2011 to 2018. The research found that Fintech has a significant mitigation effect on corporate financing constraints, and the coverage capability of Fintech has a stronger mitigation effect compared to the depth of use. Mechanism research shows that the “technology enabling” role of Fintech can alleviate the financing constraints of enterprises by reducing the degree of information asymmetry between capital supply and demand sides and reducing financing costs. Heterogeneity research shows that the mitigation effect of Fintech on corporate financing constraints is more significant in enterprises with private property, non-main board listing, senior executives with high financial literacy, and enterprises with strong competitive positions in the industry. Further research shows that, in order to identify the impact of Fintech on corporate financing types under an environment without internal control defects, Fintech enables enterprises facing financing constraints to obtain more commercial credit and bank loans; at a time when it is difficult to obtain bank loans, commercial credit has become an alternative financing method of bank loans, promoting the transfer of credit resources from traditional mortgage guarantees to enterprise commercial credit. This study provides a perspective for the research on how Fintech alleviates corporate financing constraints, and it reveals the characteristics of digital empowerment in the development of China’s capital market, providing a theoretical basis and evidence supporting the formulation of relevant policies.

An empirical study of financial securities market based on three factor model

Recent years, with the continuous development and regulation of Chinese security market, the influence of Chinese financial security market income factor is also being improved, but how to guide the correct operation of Chinese security market and improve the stock income, has become the main analysis research content of Chinese security market. Three-factor model technology provides a certain basis for the selection, prediction, decision and evaluation of financial portfolio. The three-factor model is not only widely used in our financial security market, but also can help listed enterprises to analyze and forecast our financial security market in a convenient tool form, such as: In our country's financial security market, the stock yield of small-scale companies is obviously higher than that of large-scale companies, or even value stock is higher than that of growth stock. This paper mainly analyzes and studies the operating direction of financial securities market through the three-factor model technology.

Do Financial Liabilities Matter in “Size Effect”? Evidence from the Chinese A-Share Market

Financial liabilities, as an important part of the capital structure, are closely related to the value creation and scale of growth of a company. To test whether financial liabilities affect the “size effect” of company value, this paper employs the enterprise value factor, i.e., a size factor increasing financial liabilities, to study the performance of the company in the Chinese securities market. Taking the stocks of listed companies in the Chinese A-share stock market from 2000 to 2019 as samples, this paper applies the asset pricing models for an empirical study of enterprise value. These results show that: (i) the excess return of the portfolio constructed by the enterprise value is negative and significantly different from zero, which presents the robustness of “size effect” on the attribute of financial liabilities; (ii) additionally, the “size effect” also exists in the subsamples after the financial events occurred; (iii) the results still robust while controlling the risk factors of the asset pricing models respectively.

CEO duality, CEO compensation, and earnings management: Evidence from China

This paper investigates whether chief executive officer (CEO) duality and CEO compensation influence earnings management. We obtain the data of the A-shares companies listed in both the Shanghai Stock Exchange (SSE) and Shenzhen Stock Exchange (SZSE) from the Chinese Securities Market and Accounting Research (CSMAR) database. Using a sample of 8,419 firm-year observations of 881 unique listed companies in China from 2011–2022, we find that the firms with CEO duality exhibit a higher level of earnings management, through both discretionary accruals and real activities. In addition, CEOs with higher cash-based compensation are more likely to engage in earnings management, while CEOs with higher equity-based incentives are less likely to engage in real earnings management. We further examine the moderating role of CEO duality and find that CEOs who also chair the board are more likely to engage in real earnings management. Additional analysis shows that state-owned companies are less likely to engage in earnings management activities. However, equity-based compensation provides incremental incentives for the CEOs of state-owned companies to manage earnings through accruals rather than real activities.

Stock index direction forecasting using an explainable eXtreme Gradient Boosting and investor sentiments

In this article, an explainable eXtreme Gradient Boosting (XGBoost) method is proposed for stock index prediction and trading simulation in the Chinese security market. Sentiment features of three types of investors, including institutional, individual, and foreign investors, are utilized as explanatory variables, and a binary classification model based on XGBoost is constructed to predict the direction of the Shanghai composite index and Shenzhen composite index movements. Additionally, the Gain function of XGBoost and SHapley Additive exPlanations (SHAP) are employed to estimate the importance of sentimental factors affecting index direction forecasting. Experimental results demonstrate that the XGBoost-based approach using multiple investor sentiments achieved the best forecasting accuracy, and sentiment features of the institutional investor were relatively more essential than the individual investor and foreign investor sentiments for index direction prediction in most out-of-sample periods. It demonstrates that the method proposed in this research can provide useful references for market participants to support their investment in the Chinese security market.

A Review Study on the Analysis of Validity Validation of Chinese Stock Market Pairs Using CAPM

Since the inception of the Capital Asset Pricing Model (CAPM), numerous empirical studies have been conducted to test its effectiveness. However, there are several disputes surrounding the CAPM model's implementation and study in the Chinese securities market. This paper first describes the significance of the CAPM model in testing its validity in the Chinese market. Then the paper summarizes 11 papers that include using data from the Shanghai and Shenzhen stock exchanges as assistance, and using different analytical methods to conduct research. The investigation demonstrates that the CAPM model's relevance and validity in the China stock market are restricted. Finally, a summary of the issues with the CAPM model's practical use in the Chinese securities market is provided, along with suggestions for solutions.

Comparison of the Markowitz Model and Index Model for Portfolio Optimization in the Chinese Stock Market

Markowitz Model (MM) and Sharpe’s Single Index Model (SIM) are two classical and practical models in portfolio theory. Currently the Chinese stock market is booming, therefore, it is worth testing whether MM and SIM can effectively spread the risk in Chinese security market. This paper establishes the MM and SIM based on the monthly observations of 10 stocks and CSI300. During the analysis process, there is an additional optimization constraint that selling short is not allowed. Finally, some conclusions are obtained: first, the constraint has a significant impact on the optimal portfolio, while the minimum-variance portfolio is very little affected. What’s more, although both MM and SIM can effectively disperse the specific risk, SIM have better performance in the Chinese stock market by providing higher rewards given the same risk level and bearing lower risk given a certain return. These conclusions are of great significance to investors in the Chinese security market, which can help them obtain the maximum utility through asset diversification.

Stock Price Crash Warning in the Chinese Security Market Using a Machine Learning-Based Method and Financial Indicators

Stock price crashes have occurred frequently in the Chinese security market during the last three decades. They have not only caused substantial economic losses to market investors but also seriously threatened the stability and financial safety of the security market. To protect against the price crash risk of individual stocks, a prediction and explanation approach has been proposed by combining eXtreme Gradient Boosting (XGBoost), the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and SHapley Additive exPlanations (SHAP). We assume that financial indicators can be adopted for stock crash risk prediction, and they are utilized as prediction variables. In the proposed method, XGBoost is used to classify the stock crash and non-crash samples, while NSGA-II is employed to optimize the hyperparameters of XGBoost. To obtain the essential features for stock crash prediction, the importance of each financial indicator is calculated, and the outputs of the prediction model are explained by SHAP. Compared with the results of benchmarks using traditional machine learning methods, we found that the proposed method performed best in terms of both prediction accuracy and efficiency. Especially for the small market capitalization samples, the accuracy of classifying all samples reached 78.41%, and the accuracy of identifying the crash samples was up to 81.31%. In summary, the performance of the proposed method demonstrates that it could be employed as a valuable reference for market regulators engaged in the Chinese security market.

Long-term General Asset Allocation for individual investors in Chinese securities market

Facing the boom of online transaction applications in Chinese securities market, individual investors in China vacillate between different assets while allocating assets. On account of the individual investors’ inferior ability to take risk, portfolio will be their best choices. However due to their unacquaintance to modern portfolio theory, individual investors need some instructions. As proved by Brinson in 1986, over 90% of the success of a portfolio owe to general asset selection. Hence, this paper primarily focused on providing suggestions to individual investors on general asset selection to help them to construct a portfolio in China securities market at present. Initially, this paper utilized the Mean-Variance model and CML approach to depict the market and analyzed the long-term asset allocation of multi-asset for individual investors. Based on the results, this paper suggested that individual investors should focus more on bond asset: with a portfolio of 97.37% of bond asset and 2.63% of stock asset, investors will be able to acquire a yield of 4.54% (annualized). And through this study, this paper found that Chinese securities market is not mature enough and teemed with regulations, making it to be competent than other mature market.

Dynamic forecasting of the Shanghai Stock Exchange index movement using multiple types of investor sentiment

Direction forecasting of stock market movements using the market investor sentiment is a significant subject in the research area of financial market. In this study, a novel integrated machine learning approach LightGBM-NSGA-II-SW is proposed for stock index prediction and simulation trading by combing the LightGBM (Light Gradient Boosting Machine), NSGA-II (Non dominated Sorting Genetic Algorithm-II), and SW (Sliding Window) methods. For the proposed method, first, three types of investor sentiments, including the individual, institutional, and foreign investor sentiment, are utilized as features to forecast the movement direction of the Shanghai Stock Exchange (SSE) index, and simulation trading is executed based on the direction prediction signal. Then, LightGBM is employed as the classifier for stock index direction prediction, while NSGA-II is essential for hyper-parameter optimization by incorporating multiple objectives that include the hit ratio, accumulated return, and maximum drawdown. In addition, the sliding window method is utilized to prepare the training and testing periods for realizing dynamic prediction and simulation trading of the SSE index. Finally, the proposed method generates an average hit ratio of 60.34%, an annual accumulated return of 28.43%, an average maximum drawdown of 8.35%, and a Sharpe ratio value of 3.24. The performances of the proposed method are substantially superior to the results produced by the benchmarks. Furthermore, the relative importance of the sentiment features of three types investors are comprehensively investigated, and the essential investor sentiment features for forecasting the SSE index direction in different periods are explored. Experimental results demonstrate that the proposed methodology could provide beneficial references for investors to make decisions more rationally, and it could be applied as an effective monitor of the market investor sentiment for market regulation and policymaking in the Chinese security market.