Shenzhen Composite Index Research Articles

Research on the Return Rate of Shenzhen Composite Index in my country

Research on the Return Rate of Shenzhen Composite Index in my country

Utilizing the GARCH Model for Analysis and Prediction of Stock Market Trends

This study utilizes the Generalized Auto-regressive Conditional Heteroskedasticity (GARCH) model to analyze the Shanghai Stock Exchange Composite Index and the Shenzhen Composite Index. It computes the returns and logarithmic returns, fits the GARCH model with a student-t distribution, and calculates the standardized residuals of the stock. Tests for normality and heteroskedasticity of residuals are conducted, follows by the creation of auto-correlation and partial auto-correlation plots to aid in volatility forecasting. The findings indicate that both returns of stock distributions exhibit multiple peaks and significant fluctuations, revealing that there exists pronounced volatility clustering and strong abrupt changes. Historical fluctuations significantly affect current fluctuations, showing a clustering effect. Volatility in returns has been very persistent, reflecting strong speculation and elevated overall risk. Histograms and skewness assessments show a left-skewed distribution for both indices, with a kurtosis greater than three, indicating a "fat-tailed" distribution. The positive mean values imply that China's economy is growing constantly, while the low standard deviation implies the immaturity of China's stock market. These insights highlight the dynamic behaviors and risk profiles in Chinas major stock indices, offering a deeper understanding of their speculative nature and economic implications. The Advantages and disadvantages of GARCH models and limitations of a single model are acknowledged in the report.

A novel CBAMs-BiLSTM model for Chinese stock market forecasting

The convolutional block attention module (CBAM) has demonstrated its superiority in various prediction problems, as it effectively enhances the prediction accuracy of deep learning models. However, there has been limited research testing the effectiveness of CBAM in predicting stock indexes. To address this gap and improve the prediction accuracy of stock indexes, we propose a novel model called CBAMs-BiLSTM, which combines multiple CBAM modules with a bidirectional long short-term memory network (BiLSTM). In this study, we employ the standard metric evaluation method (SME) and the model confidence set test method (MCS) to comprehensively evaluate the superiority and robustness of our models. We utilize several representative Chinese stock index data sets, namely, the Shanghai (Securities) Composite Index and the Shenzhen Composite Index, as our experimental data. The numerical results demonstrate that CBAMs-BiLSTM outperforms BiLSTM alone, achieving average reductions of 13.06%, 13.39%, and 12.48% in MAE, RMSE, and MAPE, respectively. These findings confirm that CBAM can effectively enhance the prediction accuracy of BiLSTM. Furthermore, we compare our proposed model with other popular models and examine the impact of changing data sets, prediction methods, and the size of the training set. The results consistently demonstrate the superiority and robustness of our proposed model in terms of prediction accuracy and investment returns.

Research on the Performance of the ARIMA Model in the Stock Market

With the rapid development and upgrading transformation of Chinese financial industry and increasing investment quality, the investment enthusiasm of the masses has been improved. The prediction of stock price has an effective reference for investors to determine investment strategies. In this paper, ARIMA model is used to compare the model performance of Shanghai Composite Index, Shenzhen Composite Index and Science and Technology Innovation Board. This model is also fitted to the normal period and the shock period of the stock market respectively. The study found that the Shanghai Composite Index is the most mature market, and the fitting performance of the stock market in the normal period is better than that in the shock period. From the middle of 2015 to the beginning of 2016, the Shanghai Composite Index and Shenzhen Composite Index fluctuated significantly. This study not only helps investors to adopt more reasonable investment strategies, but also has important reference value for market regulators to guide the market effectively, avoid violent fluctuations in the stock market and maintain market stability.

Research on Co-movement Between China and USA Stock Market under the Impact of COVID-19

The worldwide economic market has fluctuated wildly due to COVID-19, therefore revealing the co-movement of international stock markets should reduce its destructive force and shock. Based on the "Economic Basic Hypothesis" and "Market Contagion Hypothesis," this work uses VECM and GARCH models to examine international market volatility using the Shanghai Composite Index (SSEC), Shenzhen Composite Index (SZSE), and S&P 500 index. The total trading data of three indexes are divided into 2 periods, with September 01, 2019 as the cut-off point. The period 1 is from January 5, 2015 to August 30, 2019 and the period 2 is from September 3, 2019 to December 30, 2022. The study shows that: 1) During the COVID-19, the short-term return of both stock markets has declined and volatility has increased; 2) there is no reliable co-integration relationship between S&P 500, SSEC and SZSE which also declaims no long-run equilibrium between two markets; 3) before and during the COVID-19, the close to close return rate of America stock market guided the return rate of China stock market; 4) both S&P 500, SSEC shows an asymmetric effect and the leverage effect is distinct; 5) the co-movement was stronger when the rate of return was relatively high; 6) volatility spillover effect of China to USA stock market was significant.

Analysis of Influencing Factors of Stock Return Rate in China

In recent years, the decline of China's stock market prices, as reflected in the decline of the Shanghai Composite Index and Shenzhen Composite Index in the late 2020s, has led to an economic downturn in China. The article analysis the statistics from 2016 to 2021 and introduces the possible reasons and political background and the reasons why these factors lead to the share value. Through the method of linear regression, the possible factors were simulated on the statistical system SPSS, and a more reliable data conclusion was obtained. To analyze the causes of the economic downturn by factoring the relationship between the consumer consumption index, per capita GDP growth, Gini coefficient, the Exchange rate of RMB against the US dollar, and the Shenzhen Index. In addition, it focuses on the impact of real-life factors on the stock market, focusing on the relationship between fixed asset investment, Hong Kong factors, the economic cycle, and the stock market.

Effectiveness Analysis of Stock KDJ Indicator Method based on K-means Clustering

The application of data mining technology expands various techniques in stock investment. Among them, cluster analysis is one of the common means to study stock technical indicators. There is a problem in the current cluster analysis of stock technical indicators -- the lack of validation of large-scale stock technical indicators data sets. Most of them are suitable for the comprehensive analysis of technical indicators of a single stock or multiple stocks. Aiming at this problem, this paper takes "the validity analysis of the large-scale stock KDJ index set method based on K-means clustering" as the theme. Firstly, the k-means clustering algorithm was used to construct a deep analysis model (KDJ-k-means) for the KDJ index set of the Shenzhen Index component data group. Secondly, the K, D and J index sets of 2697 constituent stocks of Shenzhen Composite Index are analyzed experimentally. Finally, the results of integrated data mining are obtained. The KDJ-k-means model is an optimization scheme based on the KDJ index set using clustering technology, which provides an intuitive and efficient visual application for deep analysis of large-scale stock data groups.

The impacts of climate policy uncertainty on stock markets: Comparison between China and the US

The interplay between climate policy uncertainty and stock market performance has emerged as a pressing research question in light of the challenges posed by climate change to financial markets. This paper measures China's daily and monthly climate policy uncertainty (CPU) from Jan 2000 to Mar 2022 based on Chinese news data for the first time. Then, the nonlinear and lag impacts of the US CPU and China's CPU on the return, volatility, correlation and tail dependence of China's and US stock markets are investigated and compared by adopting copula function and the distribution lag nonlinear model (DLNM). The data of stock markets includes the Shanghai Composite Index (SSCI) and NASDAQ from Jan 2000 to Mar 2022 from the Choice database, and the Shenzhen Composite Index (SCI) and S&P 500 are used for the robustness test. The empirical results indicate that (1) the growth trend of China’s CPU index is similar to that of the US. However, there are significant differences between the impacts of these two CPUs on stock markets. (2) For China, high CPU decreases current stock market return and increases volatility but decreases it in the future. It could also increase the upper tail dependence between China’s and the US stock markets’ volatilities in current period. (3) For the US, CPU decreases stock market return in the short term but increases it in the long term. High CPU increases volatility in short term, decreases volatility in 5 months and increases it again after 6 months. Both low and high CPU could increase the correlation between China's and US stock markets' volatilities.

Do Bubbles in the Bitcoin Market Impact Stock Markets? Evidence From 10 Major Stock Markets

In this paper, we examine the effect of explosive behaviors in the Bitcoin market on the top 10 largest stock markets of developed and emerging countries. The daily dataset, including the Dow Jones Industrial Index (DJIA), Nasdaq (NSQ), Shanghai Composite Index (SSE), Nikkei 225 (N225), Hang Seng Index (HSI), Shenzhen Composite Index (SZSE), Euronext Amsterdam Index (AEX), London Stock Exchange (LSE), Toronto Stock Exchange (TSX), and Bombay Stock Exchange (BSE), spans July 21, 2010, to December 9, 2022. We first investigate the existence of explosive price behaviors using the bubble detection test of Phillips and Shi and the results provide evidence of multiple bubble episodes, coinciding with the monetary policy actions of the FED and ECB. Then, we address the question of whether the explosive behaviors detected affect the variance of equity returns by employing a GARCH model. The impact is negative, albeit its magnitude and significance vary among stock indices.

Stock index time series prediction based on ensemble learning model

The accuracy of stock index prediction is of great significance to national economic development. However, because of the nonlinearity and long-term dependence of stock index data, effective prediction of future stock index price becomes a challenge. In order to solve the above problems, this paper proposes a research method of stock index time series prediction based on ensemble learning model. This method first uses an Adaboost.R2 algorithm to iteratively train multiple LSTM models and then integrates these LSTM models based on the parameters obtained by iterative training. Finally, it uses the ensemble model to predict stock index time series data. This paper uses the Shanghai Composite index, CSI 300 index and Shenzhen Composite index as experimental data sets, and uses the BP model, CNN model and LSTM model as comparative models to conduct an experimental analysis. The experimental results show that the new ensemble learning model proposed in this paper has certain advantages in the research of stock index time series prediction.

A model fusion method based on multi-source heterogeneous data for stock trading signal prediction

In the prediction of turning points (TPs) of time series, the improved model of integrating piecewise linear representation and weighted support vector machine (IPLR-WSVM) has achieved good performance. However, due to the single data source and the limitation of algorithm, IPLR-WSVM has encountered challenges in profitability. In this paper, a model fusion method based on multi-source heterogeneous data and different learning algorithms is proposed for the prediction of TPs (MF-MSHD). Multi-source heterogeneous data include weighted unstructured and structured information with different granularities. RF, WSVM, BPNN, GBDT, and LSTM are selected to be the learning algorithms. The differences among meta-models are constructed by different inputs and algorithms as much as possible, and a model fusion rule is designed to determine the final TPs. Moreover, the TPs are generated based on the characteristics of individual stock. For sentiment analysis, a more accurate sentiment dictionary of stock market comments is established. Specifically, the fine-grained data is introduced to jointly determine the accurate trading moment. The prediction level of the proposal improves the accuracy and profitability, and also outperforms the composite indexes. Experimental results show that the profit rate of randomly selected stocks in MF-MSHD reaches 0.5172, while the highest value is 0.2841 in single meta-model and 0.0992 in buy and hold strategy, respectively. The other indicators including the accuracy are also modified. Compared with the increases of 0.1648, 0.4051, and 0.3397 in Shanghai Composite Index, Shenzhen Composite Index, and CSI 300 Index, MF-MSHD shows higher profitability in stock trading signal prediction.

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.

Financial Time Series Prediction Based on EMD-SVM

The market plays an essential role in the national economy and society, and people pay more and more attention to the investment in the capital market. Every valuable investment needs the guidance of scientific theory. With the improvement of China's securities market system, more and more researchers have conducted in-depth research focusing on the development of the stock market. This paper will study the stock price prediction algorithm, select the Bank of Ningbo as the research object, and propose a prediction model based on the combination of EMD and SVM algorithms. This paper first decomposes the original sequence by EMD and comprehensively considers the three related factors of the Shanghai Composite Index, Shenzhen Composite Index, and China Merchants Bank in the prediction model. Then, the SVM model is used to predict multiple decomposition sequences, respectively, and the prediction results are obtained after integrated processing. Then, regression analysis is carried out on them, and the weight is designed according to the constructed regression analysis model. The final prediction result is obtained after reconstruction. Compared with the basic SVM model, the combined prediction model constructed in this paper has a better prediction effect, and the prediction results are more objective and scientific.

Shenzhen Composite Index based on GARCH class model Research on Stock Market Volatility

Based on the review of ARCH/GARCH models, this paper uses the GARCH model to empirically study the stock market volatility of Shenzhen Composite Index, uses the GARCH-M model to analyze the risk premium, and uses the EGARCH model to empirically study the asymmetry of stock market volatility.The results show that the GARCH model can eliminate the heteroscedastic property of the residuals, the stock market volatility has a strong impact, the return risk premium is not significant, the volatility caused by bad news in the stock market is much larger than that caused by the same size of good news, there is obvious leverage effect.The GARCH model has a good prediction effect.Finally, some relevant conclusions and policy suggestions are given.

Does Chinese Exchanges Affect Bitcoin Price?

China was one of the first nations to implement cryptocurrencies back in 2013. The highest trade volumes have previously been reported on the Chinese exchanges in cryptocurrencies. However, after four years, the China government shut down cryptocurrency exchanges and initial coins offering websites. The problem arises as to whether the Chinese exchange affects the price of Bitcoin in case of prohibition. Therefore, the aim of this research is to analyse the causal link between the Chinese exchange and the Bitcoin prices. The causality between the Bitcoin price and Chinese stock market are examined through the vector autoregressive model (VAR). There is short run causality from Shanghai Composite Index and Shenzhen Composite Index to Bitcoin price. This study found that Bitcoin could be a potential hedge against the Shenzhen Composite Index while the Shanghai Composite Index is merely a factor that affects Bitcoin price in the short run. In contrast to existing studies, this study considers the causality between the Bitcoin price and the Chinese exchanges.

An Improved Elman Network for Stock Price Prediction Service

The rapid development of edge computing drives the rapid development of stock market prediction service in terminal equipment. However, the traditional prediction service algorithm is not applicable in terms of stability and efficiency. In view of this challenge, an improved Elman neural network is proposed in this paper. Elman neural network is a typical dynamic recurrent neural network that can be used to provide the stock price prediction service. First, the prediction model parameters and build process are analysed in detail. Then, the historical data of the closing price of Shanghai composite index and the opening price of Shenzhen composite index are collected for training and testing, so as to predict the prices of the next trading day. Finally, the experiment results validate that it is effective to predict the short-term future stock price by using the improved Elman neural network model.

Comparison of SME stock portfolio and manufacturing company performance in the Indonesian, Indian and Chinese stock markets

PurposeThe purpose of this study is to determine and compare the performance of small and medium enterprises (SME) and manufacturing company stock portfolios in the Indonesian, Chinese and Indian capital markets by the Sharpe Index and the significance of differences in average performance in the capital market.Design/methodology/approachThis is comparative research that compared the performances of SME and manufacturing company stock portfolios in Indonesian, Chinese and Indian capital markets. The hypothesis examination of comparative test used one-way ANOVA technique on the performance of SME and manufacturing company stock portfolios in Indonesian, Chinese and Indian capital markets. One-way ANOVA test was used in the analysis to test the average difference of performance indices of SME and manufacturing company stock portfolios is in Indonesian, Chinese and Indian capital markets.FindingsThe performance of SME and manufacturing company stock portfolios in Indonesian capital market was not better than the performances of IHSG and LQ45 Index, the performance of SME and manufacturing company stock portfolios in Chinese capital market (SZSE) was better than the performance of Shenzhen Composite Index and the performance of Shenzhen A-Share Stock Price Index. The comparison of the performances of SME and manufacturing company stock portfolios in Indonesian, Chinese and Indian capital markets showed that the performance of SME and manufacturing company stock portfolios in Chinese capital market was the best and the performance of SME and manufacturing company stock portfolios in Indonesian capital market was the lowest.Practical implicationsThe implication of this study was that SME and manufacturing company stock portfolios had relatively better performances in China and India, so investors should consider investing in SME and manufacturing company stocks. The performance of SME and manufacturing company stock portfolios in Indonesia was not able to exceed market and LQ45 portfolios, so the authority in Indonesia financial market should consider developing a special market for SME and manufacturing company to support the development of SME and manufacturing company in Indonesia and solve the problem of lack of funding source for SME and manufacturing company.Originality/valueThe originality of the present study is in the measurement of the performance of SME and manufacturing company stock portfolio by risk-adjusted return which returns per risk unit measured by Sharpe Index as a more beneficial measurement in measuring stock portfolio performance than average return. Comparative study of the stock portfolio performances of small medium enterprises and manufacturing company In Indonesian, Chinese and Indian stock markets, and object studies conducted in Indonesia, China and India.

Linkages Between Chinese Stock Price Index and Exchange Rates-An Evidence From the Belt and Road Initiative

This paper selects the daily data of the exchange rates of Chinese Yuan (CNY) over the currencies of 14 countries along the Belt and Road, Shanghai composite index and Shenzhen composite index to study the influence of the Belt and Road Initiative on the linkages between exchange rates and Chinese stock index based on the flow-oriented model and the stock-oriented model. To reflect the fluctuations in daily data and reduce the central bank's interference with the exchange rate, two fuzzy techniques are used to process data, that is, the centroid based measure and the integral based measure. Then we judge the relationship between exchange rate and stock index through the Pearson correlation coefficient and the Granger causality test. Besides, we further compare the results and their differences by the classic crisp method and our two fuzzy techniques, which enable us to judge their correlation more accurately, and provide a reference for a wider application of the proposed fuzzy methods. We find that there is a correlation between exchange rate and stock index under certain conditions, and the Belt and Road initiative strengthens the relationship between the Chinese foreign exchange market and the stock market, more importantly, the fuzzy techniques are effective to judge this relation.

Model Averaging Estimation for Conditional Volatility Models

This paper is concerned with the model averaging estimation for the conditional volatility model family. We propose a model averaging estimator for the conditional volatility under a framework of zero conditional mean and construct the corresponding weight choosing criterion. It is shown that the weight selected by the criterion asymptotically minimizes the true KL divergence as well as the Itakura-Saito distance. The forecast of the conditional volatility is also constructed under the model averaging framework. As a generalization, we extend the method to the framework where both the conditional mean and volatility require estimation. On this condition, we propose the double-weight averaging estimator that assigns separate weights to the conditional mean and volatility. Monte Carlo experiments are in favor of our newly-proposed method. For empirical application, we apply the model averaging method to the forecast of the daily volatility of Shanghai Composite Index and Shenzhen Composite Index between March 10, 2016 and June 1, 2018. We find that compared with other commonly-used methods, the model averaging forecast leads to the highest forecast accuracy even under different loss functions.

A Causality Analysis of Societal Risk Perception and Stock Market Volatility in China

Modern China is undergoing a variety of social conflicts as the arrival of new era with the transformation of the principal contradiction. Then monitoring the society stable is a huge workload. Online societal risk perception is acquired by mapping on-line public concerns respectively into societal risk events including national security, economy & finance, public morals, daily life, social stability, government management, and resources & environment, and then provides one kind of measurement toward the society state. Obviously, stable and harmonious social situations are the basic guarantee for the healthy development of the stock market. Thus we concern whether the variations of the societal risk are related to stock market volatility. We study their relationships by two steps, first the relationships between search trends and societal risk perception; next the relationships between societal risk perception and stock volatility. The weekend and holiday effects in China stock market are taken into consideration. Three different econometric methods are explored to observe the impacts of variations of societal risk on Shanghai Composite Index and Shenzhen Composite Index. 3 major findings are addressed. Firstly, there exist causal relations between Baidu Index and societal risk perception. Secondly, the perception of finance & economy, social stability, and government management has distinguishing effects on the volatility of both Shanghai Composite Index and Shenzhen Composite Index. Thirdly, the weekend and holiday effects of societal risk perception on the stock market are verified. The research demonstrates that capturing societal risk based on on-line public concerns is feasible and meaningful.