Shanghai Stock Exchange Composite Index Research Articles

Like a moth to a flame: Do stock market bubbles exacerbate credit risks of peer‐to‐peer lending?

AbstractWe establish a causal link between stock market bubbles and credit risks from peer‐to‐peer lending. Employing a fuzzy regression discontinuity design based on retail investors' disproportional increase in stock market participation when the Shanghai Stock Exchange composite index exceeds 3500, we find that both the default rate and the degree of delinquency rise disproportionately for loans borrowed above the 3500 threshold. This effect is more pronounced among loans of lower quality and when borrowers are more overconfident and less risk averse. Overall, our results suggest that FinTech developments could amplify financial risks and induce contagion across markets.

Stock Reverberation Due to International Trade Related Economic Factors

The economic activities of a nation are measured through various macroeconomic factors such as GDP, EXIM rate, unemployment level, Wholesale Price Index (WPI), and Consumer Price Index (CPI). Numerous studies have focused on identifying the association between inflation and stock prices, with some also highlighting the relationship between gold prices and stock price volatility. This study uses exchange rate and balance of trade as indicators to predict stock price volatility in China. The stock price volatility is measured using the Shanghai Stock Exchange Composite Index (SSE Composite Index). An analytical method is employed to evaluate the involved variables, with Johansen integration used to determine the relationships between them. The Vector Error Correction Model (VECM) is utilized to model these relationships. The Granger causality test and Johansen co-integration test measure the causal relationships of economic variables on the stock market indices. The findings indicate that the exchange rate positively influences stock prices, whereas the balance of trade shows a negative relationship. The exchange rate Granger-causes stock prices, but the reverse is not true. A similar Granger-causing relationship exists between the balance of trade and stock prices. The study also reveals that while the exchange rate of RMB to USD may not have a strong impact on stock prices, the exchange rates of other currencies may significantly influence China's stock prices.

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.

Spillover Effect Due to Macroeconomic Variables: Evidence from Volatility in BRICS Nations

Purpose: The aim of this study is to examine the spillover effect of volatility in macroeconomic variables on indices of emerging nations. Theoretical Framework: This research considered macroeconomic variables such as GDP, FDI, Inflation, and money supply and indices of emerging nations (Brazil, Russia, India, China and South Africa). This paper focuses on identifying how stock markets of BRICS are affected by variations in macroeconomic variables. Design/Methodology/ Approach: The data on GDP, inflation rate, FDI, Money Supply, and Indices (Chinese BOVESPA, Russian, Indian Bombay Stock Exchange, Chinese Shanghai Stock Exchange (SSE) Composite Index, and South African FTSE) was drawn for a period of 20 years from 2001 to 2021. Prices of the indices were acquired from www.investing.com. Macroeconomic indicators data was taken from the www.worldbank.org. For analysis, the VECM and VAR models are prepared. Findings: The findings show that all the economic variables are considerably leaving a spillover impact on the prices of BRICS nations. VECM models state relationship among the GDP, FDI, Inflation, and money supply and Stock markets of Brazil, Russia, India, and China. However, in the case of South Africa, these economic variables have a dynamic correlation with its stock market index as is depicted through the VAR Model. Research Practical and Social implications: The study addresses the issue of spillover effect of factors which had indirect impact on the volatility of the indices of BRICS nations. In the times of such high level of uncertainties like covid pandemic it is important to understand the direct and indirect factors and variables which may impact the variability of the stock markets. Originality/Value: This articles has conducted an extensive analyses of macroeconomic variables and their impact on indices of emerging nations. The authors provides clarity and insights in understanding the variability in markets in depth.

Research on Prediction and Analysis of Shanghai Stock Index Based on the ARIMA Model

This study aims to analyze the future trends of the Shanghai Composite Index (SSE Composite Index), a primary stock index on the Shanghai Stock Exchange (SSE). The SSE Composite Index not only serves as a crucial indicator of the Chinese capital market but also attracts global attention as a focal point in financial markets worldwide. Its fluctuations have broad impacts, influencing decisions of investors and the global economic environment. Due to the increasing limitations of traditional forecasting methods such as moving averages and exponential smoothing in the face of the complexity and uncertainty of financial markets, this paper chooses to employ the Autoregressive Integrated Moving Average (ARIMA) model for more accurate and sophisticated predictions. The paper provides a literature review to outline relevant studies, delves into the specific processes of data preprocessing and model establishment, and concludes with empirical analysis and result discussions. The study suggests that the ARIMA model remains effective and feasible in predicting the SSE Composite Index.

The Multifractal Phenomenon of Stock Price Caused by “Tesla Rights Defense Event”

In this paper, we explore the periodicity of the multifractal phenomenon of stock price caused by public relations events using the multifractal detrended fluctuation analysis (MF-DFA) method. The Tesla stock closing price in the context of the Tesla rights defense event is used to obtain the generalized Hurst exponent and multifractal spectrum. We find that the stock price exhibits multifractal characteristics. Afterwards, we conduct a multifractal time-varying analysis using high-frequency data for 12 time periods before and after the Tesla rights defense event, which happened on 19 April 2021. We calculate the [Formula: see text] and [Formula: see text] before and after the rights defense event to measure the degree of multifractal of Tesla stock price. In addition, we also compute the difference between the two [Formula: see text] and two [Formula: see text], which are regarded as [Formula: see text] and [Formula: see text]. The results show that the Tesla stock market efficiency degree raised as the period time increases until the eighth period. And after the eighth period, both [Formula: see text] and [Formula: see text] tend to be stable, and the degree of market efficiency caused by the Tesla rights defense event remains almost unchanged. In addition, we use three broad market indices in the context of the Russia–Ukraine conflict to test the universality. The three broad market indices are Dow Jones Industrial Average (DJIA), Shanghai Stock Exchange Composite Index (SSEC) and Nikkei 225 ETF (N225). The results demonstrate that with the extending of time period, [Formula: see text] and [Formula: see text] gradually stabilize, indicating that public memorability will diminish and the market efficiency almost remains unchanged.

Analisis Kausalitas Nilai Tukar Rupiah/USD, Dow Jones Industrial Average (DJIA), Nikkei225, Shanghai Index Composite (SSEC) Terhadap Indeks Harga Saham Gabungan (IHSG)

The COVID-19 pandemic has had a negative effect on global stock markets. In addition, the increase in the number of confirmed cases of COVID-19 further increased the volatility of the exchange rate. This study aims to determine whether there is a causal relationship between the Exchange Rate, the Dow Jones Industrial Average (DJIA), the Nikkei225 Index, and the Shanghai Stock Exchange Composite Index (SSEC) and the Composite Stock Price Index (CSPI). The research method used is the Augmented Dickey Fuller Unit Root Test, and Granger Causality. The results of the study using Granger Causality indicate that the Exchange Rate, DJIA Index, and Nikkei225 Index have a causal relationship to the Composite Stock Price Index. The SSEC index does not have a causal relationship with the Composite Stock Price Index. This means that the JCI movement has the influence of the Exchange Rate, the DJIA Index, and the Nikkei225 Index.

Portfolio Optimization: A Return-on-Equity Network Analysis

This article proposes return-on-equity (ROE) networks for portfolio optimization, which integrate the DuPont analysis and graph theory. Portfolio diversification is interpreted as follows: An intercluster relationship of the network structure diversifies business models, whereas an innercluster relationship variegates different industries. The proposed approach is applied to the Chinese stock market. It shows that, in terms of the annualized return, the ROE network optimized portfolio reached <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.20\%$</tex-math> </inline-formula> compared with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.02\%$</tex-math> </inline-formula> of the Shanghai Stock Exchange (SSE) Composite Index. It also shows that portfolios with 100–200 stocks, which are composed of the top 10%–20% ROE stocks, reached the highest return-risk efficiency.

Stock Market Integration among Trading Partners

This study makes an attempt to analyse the dynamics of integrating relationship shared by Indian and Chinese stock market for the period ranging from January 01, 2002 to June 30, 2021. Prominent stock indices namely BSE SENSEX index of Bombay Stock Exchange Limited, India and Shanghai Stock Exchange Composite Index (SCI) of Shanghai Stock Exchange Limited, China were considered. Johansen Cointegration test was used to analyse the long term integration shared by the stock markets. Granger causality test was employed to know the causal relation shared by the stock markets. Presence of long term integration and unidirectional causal relation between the stock markets was found. It may be inferred that investment diversification among these markets may not provide fruitful returns to the investors in long run. Presence of causality from BSESENSEX to SCI and absence of causality from SCI to BSESENSEX suggests significant flow of information from Indian stock market to the Chinese stock market but not vice-versa. Findings of the study also suggests that extent of connectedness shared by the Indian and Chinese market does not vary too much with time. The findings revealed that although the financial markets of trading partners share significant integration but the intensity of trade does not impact the extent of financial linkages shared by the nations. These implications may provide significant insights to the investors, fund’s managers, policymakers and other market players.

Extreme Connectedness Across Chinese Stock and Commodity Futures Markets

This study examines the price spillovers and connectedness among stock markets, the Shanghai Stock Exchange Composite (SSEC) index, Hang Seng index, Shenzhen Stock Exchange (SZSE) index, commodity futures markets, Aluminum (AL), Gold (AU), Copper (CU), Zinc, Steel Rebar, and Natural Rubber in bearish and bullish market situations. We use the quantile connectedness approach of Ando et al. (2022) to calculate hedge coverage ratios, optimal portfolio weights, and hedge coverage effectiveness. Our empirical analysis yields several important results. First, there is no difference in the results of the TVP-VAR-DY and TVP-VAR models in relation to the magnitude of return transmission during the sample period. Second, CU is the main transmitter and AU is the main receiver of shocks from the network. Third, under the QVAR method estimations, the CU (AU) is the major (minor) contributors to the network during the normal, bearish, and bullish market statuses. Fourth, the results of the hedge ratio strategy confirm that CU and AU are respectively the most and the least expensive assets for a long-term investment in the Chinese stock market in different market conditions.

Profitability of technical trading rules in the Chinese stock market

This article examines the profitability of technical analysis on the Shanghai Stock Exchange Composite Index (SSEC) and the Growth Enterprise Market (GEM) Index, employing stepwise generalized error rate control procedures to address the data-snooping bias. Our comprehensive study encompasses 38,456 trading rules, extending beyond prior research by incorporating simple rules alongside their contrarian and complex counterparts. Upon rectifying data-snooping bias, we show that only a few complex rules outperform SSEC in different testing sub-periods, and no trading rules outperform GEM. As the outperforming rules are based on relatively high frequent trades, most lucrative rules yield negative returns in the out-of-sample periods once transaction costs were considered. Our new findings shed light on the efficiency of the Chinese stock market from 2010 to 2021.

IPO Fund Raising and Chinese Markets IPO Suspension

During the period spanning from 2007 to 2021, China had substantial increase in its Gross Domestic Production (GDP). However, it is noteworthy that this rise was not mirrored by a commensurate expansion in China's A-share market. When examining the performance of China's stock market in relation to other major global economies within the same time frame, it becomes evident that the overall returns of China's stock market are comparatively lower. For example, Standard &amp; Poors 500 increased 236.11%, Nasdaq Composite Index increased 547.75%, the German Stock Index 30 increased 109.99%, and Nikkei Stock Average (Nikkei 225) increased 66.21%, whereas Shanghai Stock Exchange Composite Index increased 33.41%. Based on the monthly Initial Public Offering (IPO) proceeds of China's stock market from 2007 to 2021, this paper comprehensively examines the impact of IPO activities on China's stock market, including the correlation coefficients among IPO fundraised, index amplitude, index return, and GDP. By studying the correlation between the Chinese All-securities Index (CSI) data and IPO fundraising, as well as exploring the impact of policy changes on the market, it can be concluded that IPO fundraising itself has a limited impact on market returns. The low yield in China's country's stock market might be due to corporate information asymmetry, corporate information fraud, and imperfect supervision. The contributions of this paper include the summary and interpretation of China's IPO and related data, the interpretation of China's IPO reform and other policy actions, and the suggestions for the future development direction of China's market.

Trend Forecast of Shanghai Stock Exchange Composite Index Based on Monetary Supply and Consumer Price Index

Stock market indices often serve as indicators of a country's economic conditions. Therefore, analysing the trends of stock market indices can assist individuals, institutions, and even governments in comprehending the state of the economy and developing suitable investment strategies or economic policies. However, accurately predicting these indices poses a significant challenge. In recent years, machine learning has displayed remarkable learning capabilities in various industries, making it an intriguing and viable avenue for trend prediction. In this article, we have selected two closely linked data sources, namely monetary supply and consumer price index, which are highly correlated with economic operations. By combining these data with regression models, we have developed an algorithm for predicting China's Shanghai Stock Exchange Composite Index (SSECI). Experimental results illustrate a strong correlation between the collected data and the index, highlighting their value in indicating economic conditions.

Forecasting multistep daily stock prices for long-term investment decisions: A study of deep learning models on global indices

Deep machine learning algorithms play an important role in facilitating the development of predictive models for the stock market. However, most studies focus on predicting next-day stock prices or movements, limiting the usability of the predictive model for investors. This study extensively explores the ability of deep learning models to predict out-of-sample the daily prices of global stock indices over a long term, up to a year. The performance of six models, including Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN), are compared using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The models predict the long-term daily prices of five global stock indices, namely the Nifty, the Dow Jones Industrial Average (DJIA), the DAX performance index (DAX), the Nikkei 225 (NI225), and the Shanghai Stock Exchange composite Index (SSE). The results confirm the superiority of LSTM for predicting long-term daily prices. The Bi-LSTM does not improve the result of LSTM but performs better than other algorithms. CNN overfits the training data and poorly forecasts the long-term stock prices of global indices on the testing data. This research demonstrates the potential of deep learning models for long-term stock price forecasting, offering valuable insights for investors. Additionally, the patterns of predicted daily prices can be helpful in building trading and risk management decision systems.

Shanghai Stock Exchange Composite Index Prediction Using Macro Variables Based on ARIMAX Model

Stock market prediction has always been a prevailing topic among investors and researchers. Among numerous market index, the Shanghai Stock Exchange Composite Index (SSE Index) is recognized as one of the most indicative stock indexes in China's A-share market. As it is discovered that the fluctuations of SSE index and exchange rate (USD/CNY) displays highly similar pattern since the subprime mortgage crisis, this study aims to use macro variables (including exchange rate) to predict the SSE index based on ARIMAX model. The data are collected since 2006 and based on which ARIMA (14,1,4) model is generated. Distinct macro variables are added in this ARIMA model respectively and it concludes that the incorporation of exchange rate with certain lags can significantly increase the fitting degree. Cross validations on different lengths of validation sets are implemented, which shows that the model can make accurate forecast in relatively short term. The result manifests increasing accuracy when incorporating certain explanatory regressive factors, which might provide valuable and enlightening information in short term for researchers or investors.

The Impact of China's Pandemic Deregulation Policy on SSEC and SZSE Indexes

In early stages of the COVID-19 outbreak, Chinese stock market experienced a sharp decline as investors became increasingly concerned about the economic consequences of the pandemic. As the pandemic continued to spread globally, the Chinese government implemented strict measures to control its spread, which included lockdowns, travel restrictions, and other forms of social distancing. Although these measures worked, economy was drastically affected with many stores closing down and consumer expenditures significantly declining. This, meanwhile, led to a decrease in corporate profits and a reduction in investor confidence. However, on December 8, 2022, the Chinese government issued a pandemic deregulation policy identifying people would return to their normal life. In this paper, prices of the Shanghai Stock Exchange Composite (SSEC) index and Shenzhen Securities Component (SZSE) index were retrieved and ARIMA method was adopted to predict the stock prices for a period after the pandemic. The author compared forecast prices with the actual stock prices and then analyzed the implications of the deregulation policy on the stock market. These two indexes are only a snapshot of the Chinese economy, and certain informative feedback can be obtained through this study, which is helpful to relevant investors and policy makers.

Forecasting long-term stock prices of global indices: A forward-validating Genetic Algorithm optimization approach for Support Vector Regression

Predicting long-term stock index prices is a challenging and debatable task. Most of the studies focus on predicting next-day stock prices. However, those are not useful to long-term investors and traders. In this paper, we attempt to predict up to a year’s daily prices of global stock indices using daily close prices data. This study fills a gap in the existing literature by focusing on long-term stock index price forecasting, which is crucial for practical applications in the financial markets. Moreover, The empirical analysis highlights the superior performance of a rolling forward-validation approach over cross-validation in predicting long-term stock prices. A forward-validating Genetic Algorithm Optimization for Support Vector Regression (OGA-SVR) is used to efficiently forecast multi-step ahead long-term global stock indices. Further, the performance of the model is compared with that of Support Vector Regression (SVR), Grid Search based Support Vector Regression (GS-SVR), Genetic algorithm-based Support Vector regression (GA-SVM), and state-of-the-art Long Short-Term Memory (LSTM) algorithms. The models are empirically tested on five global stock indices time series daily data, namely Nifty, Dow Jones Industrial Average (DJIA), DAX performance index (DAX), Nikkei 225 (NI225), and Shanghai Stock Exchange composite index (SSE). Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used for the evaluation The result shows the OGA-SVR model outperforms other models in predicting the long-term prices of global indices. Further, the OGA-SVR model has the potential to forecast the long-term underlying future pattern of index prices which can be used to build trading and risk mitigation systems by investors and traders.

Novel optimization approach for realized volatility forecast of stock price index based on deep reinforcement learning model

Accurately predicting volatility has always been the focus of government decision-making departments, financial regulators and academia. Therefore, it is very crucial to precisely predict the realized volatility (RV) of the stock price index. In this paper, we take the RV sequences of Shanghai Stock Exchange Composite Index (SSEC), Standard & Poor 500 index (SPX) and Financial Times Stock Exchange Index (FTSE) as the research objects, and propose a predictive model based on optimized variational mode decomposition (VMD), deep learning models including deep belief network (DBN), long short-term memory network (LSTM) and gated recurrent unit (GRU), and reinforcement learning Q-learning algorithm. Firstly, the original RV sequence is decomposed by using the VMD ideal parameters optimized by grey wolf optimizer (GWO) to obtain the intrinsic mode functions (IMFs). Then, DBN, LSTM and GRU are used to predict same IMF simultaneously. Finally, the optimal weights of the above three models are determined by the Q-learning algorithm to construct an integrated model, and the final results are obtained after accumulating the predicted values of each IMF. The predictive performance of the model was evaluated by four loss functions: the mean average error (MAE), mean squared error (MSE), heterogeneous mean average error (HMAE), heterogeneous mean squared error (HMSE) and modified Diebold and Mariano test (MDM). The experimental results show that the constructed GVMD-Q-DBN-LSTM-GRU method has better performance that the comparison model in both emerging and developed markets.

A Real-Time GARCH-MIDAS model

This paper proposes the Real-Time GARCH-MIDAS model to model and forecast volatility. An empirical application to the Shanghai Stock Exchange Composite Index (SSEC) and Shenzhen Stock Exchange Component Index (SZSEC) of China shows that the Real-Time GARCH-MIDAS model outperforms competing models in terms of both empirical return fitting and out-of-sample volatility forecasting. Moreover, the superior forecasting performance of the Real-Time GARCH-MIDAS model is robust to alternative rolling windows, alternative benchmark models, alternative MIDAS lags and alternative volatility proxy. Further discussion illustrates the flexibility of the Real-Time GARCH-MIDAS model.

Sources of uncertainty and their impact on stock prices evidence from emerging economies

Abstract This study investigates the short- and long-term effects of various sources of uncertainty on the share prices of key exchanges in emerging nations. The sample comprises monthly time series data from January 2017 to December 2021 for China, India, Russia, and Brazil. The study contains a version of Autoregressive-Distributive-Lag (ARDL) with error correction as well as other relevant approaches to time series. Economic policy, climate policy, pandemics, and Twitter-based uncertainty may cause a long-term decline in SSE (Shanghai Stock Exchange) composite index and BSE (Bombay Stock Exchange) Sensex index. In China, geopolitical, climatic, and pandemic uncertainty are short-term sources of uncertainty, and in India, economic policy, geopolitical, and pandemic uncertainty. Moreover, no sources of uncertainty have a long-term impact on Russia’s Moscow Exchange (MOEX) index. All sources except climate uncertainty are short-term MOEX index contributors. Pandemics and Twitter-based uncertainty are long-term sources, whereas economic policy and Twitter-based uncertainty are short-term sources for Brazilian Stock Exchange (BOVESPA) Index. This research adds to the literature by examining the relationship between distinct sources of uncertainty and an emerging market share prices index. It provides the behavior of leading share price indexes in the presence of uncertainty. The study’s conclusions only apply to emerging economies. Future research may take into account a panel dataset consisting of a large number of emerging nations to examine the same set of variables.