Shanghai Stock Exchange Composite Index Research Articles

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.

Global market factors that impact Baltic Dry Index

The Baltic Dry Index is used as a strategic tool by shipping companies to monitor the daily movement of freight rates for the transportation of bulk cargoes on predetermined routes for the different types of bulk carriers. Therefore, the management of shipping companies pays great attention to the factors that can contribute to the prediction of the price movement of the Baltic Dry Index. Main goal of this paper is to explore if stock market indices of United States of America (S&P 500 stock index) and China (Shanghai stock exchange Composite index), 10 Year bond yield, CRB index, WTI Crude oil and Gold as global market factors, but also as leading macroeconomic global indicators, have impact on movement of BDI. We explored period from January 1, 2003 to December 31, 2021, with monthly data for which the multiple linear regression method was used to analyse mentioned global market factors impact on BDI. The research found that the movement of S&P 500 and SSECI stock indices and CRB index had a positive impact on the movement of BDI, while the movement of Gold and WTI crude oil had negative impact on BDI for the observed period. The scientific contribution of this paper is manifested through observation and exploring relationship of mentioned global market factors with BDI, previous papers observed shorter time period and included macroeconomic indicators which are lagging, together with some global market factors.

China's Reform Spree in 2021: Common Prosperity and Others

In 2021, China launched a series of reform initiatives including common prosperity, a property tax, a regulatory crackdown on technology firms, a roadmap for peak carbon dioxide emissions and carbon neutrality and other policies aiming to improve people's lives. This study reviews each of them and finds that they are centred around common prosperity and guiding resource allocation. Based on Google Trends search results, this study creatively created a series of common prosperity policy indices, showing that China in 2021 may have experienced the most important policy shift since at least 2004. Based on EGARCH and ARDL models, this study finds that the whole economy, proxied by two popular composite indices, i.e. the Shanghai Stock Exchange Composite Index and the China Securities Index 300, responded positively to the common prosperity policy. Stock market responses also show that China's policies have successfully guided the resource allocation from the soft technology sector to the hard technology one. This study also discusses the broad implications, such as the role of the government and the evolution of private ownership in the Chinese economy.

Exchange Rate and Stock Markets During Trade Conflicts in the USA, China, and India

During trade tussles, most of the stock exchanges are impacted, both directly and indirectly, thereby influencing the exchange rates of the countries. This article gives a comprehensive insight into the effects of exchange rate on the USA, China, and Indian stock market, that is, the Dow Jones industrial average index (DJI), Shanghai Stock Exchange (SSE) composite index, and Nifty50 index during trade conflicts. Trade conflicts here talk about the recent trade war between the USA and China, which was invariably fallen in line with the outbreak of COVID-19. Sample of this study comprises the daily closing price from different indices and exchange rate values US dollar, yuan, and rupee. This article uses ordinary least square (OLS) model and generalized autoregressive conditional heteroskedasticity (GARCH) model to analyze the volatility and influence of exchange rate over the stock market. Results from OLS model indicated that the fluctuations in exchange rate have minimum impact on the daily closing price of stock indices, that is, DJI, SSE, and Nifty50 in the USA, China, and India, respectively, however, exchange rate impacts volume of shares traded in all three stock exchanges. The GARCH model implies that the conditional variance is less volatile for Nifty volume, but highly volatile for SSE and DJI stock volumes traded. The findings of the study provide insights to the domestic and foreign investors regarding their investment in the stock market during trade conflicts and mostly during COVID-19 situation.

The Effect of the World Exchange Indexes (DJIA, SSEC and NIKKEI 225) on the Indonesia Stock Exchange Index (IDX)

This study aims to determine the effect of the Dow Jones Industrial Average (DJIA), Shanghai Stock Exchange Composite (SSEC) and Nikkei 225 Indexes on the movement of the Indonesian Stock Exchange Index (IDX) for the period of November 2016 – November 2021. This study uses non-participant observation methods and the whole population in that time period is used as a sample. Data analysis techniques include descriptive statistics, classical assumption test. The results of the multiple linear regression analysis showed that the DJIA and Nikkei 225 indexes partially had a positive effect on the IDX, while the SSEC Index had no effect on the IDX. However, the value of (R2) shows that only 16.6 percent of the IDX movement can be partially explained by research variables, while 83.4 percent is influenced by factors outside this study.

Stock market fluctuation and stroke incidence: A time series study in Eastern China

ObjectiveTo explore the association between daily change of Shanghai Stock Exchange (SSE) Composite Index and stroke incidence in Zhejiang, China. MethodsData on daily stock index change and stroke incidence during 2009–2016 were collected to form the time series. Data were analyzed using distributed lag non-linear model (DLNM) with quasi-Poisson as link function, controlling for long-term and seasonal trends, day of the week, public holiday, meteorological factors, and economic conditions. ResultsLarge changes in daily stock index were associated with increased risk of stroke. Rise of stock index was significantly related to higher stroke incidence without delay. The relative risks (RRs) of stroke on the lag0 day were 1.040 (95% CI 1.011–1.071) for 100 index increase, and 1.111 (95% CI 1.000–1.235) for 200 index increase. Additionally, fall of stock index was associated with higher stroke incidence at lag of 5 and 6 days. The relative risks (RRs) of stroke for −200 index decline on the lag5 and lag6 day were 1.058 (95% CI 1.025–1.093) and 1.061 (95% CI 1.019–1.104) respectively. The association was relatively consistent across subgroups stratified by the subtype of stroke, gender, and age groups. ConclusionBoth rise and fall of stock index were associated with an elevated occurrence of stroke.

Economic Policy Uncertainty and Conditional Dependence between China and U.S. Stock Markets

In this paper, we investigate the impact of economic policy uncertainty (EPU) on the conditional dependence between China and U.S. stock markets by employing the Copula-mixed-data sampling (Copula-MIDAS) framework. In the case of EPU, we consider the global EPU (GEPU), the American EPU (AEPU), and the China EPU (CEPU). The empirical analysis based on the Shanghai Stock Exchange Composite (SSEC) index in China and the S&P 500 index in the U.S. shows that the tail dependence between China and U.S. stock markets is symmetrical, and the t Copula outperforms alternative Copulas in terms of in-sample goodness of fit. In particular, we find that the t Copula-MIDAS model with EPU dominates the traditional time-varying t Copula in terms of in-sample fitting. Moreover, we observe that both the GEPU and AEPU have a significantly positive impact on the conditional dependence between China and U.S. stock markets, whereas CEPU has no significant impact. The tail dependence between China and U.S. stock markets exhibits an increasing trend, particularly in the recent years.

Measuring multi‐volatility states of financial markets based on multifractal clustering model

AbstractMeasuring multi‐volatility states is an important component of financial risk management. In this paper, taking 17 years' high‐frequency data of the Shanghai Stock Exchange Composite Index (SSEC) as an example, we set out to propose a novel model of multifractal clustering model (MCM), combining multifractal algorithm with k‐mean clustering algorithm, to measure the multi‐volatility states of financial markets. The empirical results present that the financial markets are multifractal and the multifractal parameter Sα measured by multifractal algorithm correlates well with the performance of financial markets. Meanwhile, the multi‐volatility states recognized by k‐mean clustering algorithm based on Sα have obvious statistical significance. More importantly, the experiment results show that by the loss functions and Diebold–Mariano (DM) test, generalized autoregressive conditional heteroscedasticity (GARCH) model with the multi‐volatility state measured by MCM is superior to others measured by Markov switching (MRS) model and hidden Markov model (HMM) as well as that without the multi‐volatility states, which indicates that MCM has best performance of measuring the multi‐volatility states of financial markets. The robustness of MCM is also evaluated by Shenzhen Securities Component Index (SZSE) instance set, SSEC instance sets with different time intervals, and different volatility models. The results still prove that MCM has strong robustness on measuring the multi‐volatility states of financial markets.

On Intraday Shanghai Stock Exchange Index

This paper investigates the return, volatility, and trading on the Shanghai Stock Exchange with high-frequency intraday five-minute Shanghai Stock Exchange Composite Index (SHCI) data. The random walk hypothesis is rejected, indicating there are predictable components in the index. We adopt a time-inhomogeneous diffusion model using log penalized splines (log P-splines) to estimate the volatility. A GARCH volatility model is also fitted for comparison. A de-volatilized series are obtained by using the de-volatilization technique of Zhou (1991) that resample the data into different de-volatilized series with more desired properties for trading. A trading program based on local trends extracted with a State Space model is then implemented on the de-volatilized five-minute SHCI return series for profit. Volatility estimates from both models are found to be competitive for the purpose of trading.

Performance of Shanghai Composite Index and Sector Indices in The Beginning of Novel Coronavirus Pandemic

This paper aims to quantify the effect of the deadly novel coronavirus (COVID-19) pandemic outbreak on Chinese stock market performance. Shanghai Stock Exchange Composite Index and its component sector indices are examined in this study. The pandemic is represented by a lockdown dummy, new COVID-19 cases and a dummy for 3 February 2020. First, descriptive analysis is performed on these indices to compare their performances before and during the lockdown period. Next, regression analysis with Exponential Generalized Autoregressive Conditional Heteroscedasticity specification is estimated to quantify the pandemic effect on the Chinese stock market. This paper finds that health care, information technology and telecommunication services sectors were relatively more pandemic-resistant, while other sectors were more severely hurt by the pandemic outbreak. The extent to which each sector was affected by pandemic and sentiments in other financial and commodity markets were reported in details in this paper. The findings of this paper are resourceful for investors to avoid huge loss amid pandemic outburst and the China Securities Regulatory Commission in handling future pandemic occurrence to cool down excessive market sentiments.

The Relationship Between the Chinese Stock Market, the US Stock Market, and Some Other Economic Indexes Under the COVID-19 Pandemic

The COVID-19 outbreak has had a huge impact on all sectors of the world, especially the economy. Among all the economic indicators, the stock market indexes are particularly important, because the stock markets anticipate the future economic change and reflect investor confidence and thus may be the barometer of the overall economy. The Chinese and US stock markets are the most important stock markets. Because New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotations (NASDAQ) have the greatest market cap worldwide. And Shanghai Stock Exchange (SSE) and Hong Kong Stock Exchange (HKSE) can obviously show the conditions of economy in the mainland of China and East Asia. We collected the daily increase or decrease indexes of the U.S. and China stock markets to study their relationships and how they are affected by the pandemic. To study this problem, this paper uses three methods, including Local Similarity Analysis (LSA), Least absolute shrinkage and selection operator (LASSO) and Markov chain. We calculate the correlation between the U.S. stock indexes and China stock indexes with LSA and analyze the possible factors which lead to these correlations. LASSO is used to find out other economic indexes which affect the U.S. and China stock markets and test whether these indexes can predict the trend of future markets. Lastly, Markov chains can be used to analyze the effects of the day to the next day transmission in stock markets. We find that there is a high correlation between the U.S. and the China stock using LSA, especially, from Jan.10 to Feb.10. We find that Shanghai Stock Exchange Composite Index (SSEC) and NASDAQ are correlated to the Standard and Poor’s 500 Index (S&P500) in the US market. S&P500, confirmed cases of COVID-2019 in China (Chinese cases), USD to CNY exchange rate are correlated to the SSEC in the Chinese market. Using these indexes, the future indexes of the U.S. stock market can be predicted at a PCC correlation, the highest is 0.44 and the lowest is 0.24 for the China market. LASSO shows that the U.S. market can be more accurately predicted by the indexes. The estimated Markov models of the two stock markets are pretty similar, i.e. the probability that the value of the U.S. stocks increases two days in a row is 13.40% and the Chinese market is 11.34%. To sum up, we find that the two stock markets are highly correlated under the Covid-19 pandemic despite from some differences.

Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market

With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network.

Impact of Wuhan Lockdown in Early Stage of COVID-19 Outbreak on Sector Returns in Chinese Stock Market

The unprecedented Wuhan lockdown due to the outbreak of the COVID-19 pandemic provides a natural experiment that will elucidate its immediate impact on the stock market. Event study methodology is adopted to identify any short-run abnormal returns in the Shanghai Stock Exchange Composite Index and its ten component sectors. This paper reports empirical evidence on the negative short-run impact of the Wuhan lockdown in the face of the pandemic outbreak on all component sectors of the Shanghai Stock Exchange Composite. The health care and information technology sectors, which helped considerably in the fight against the pandemic, were resilient and outperformed the general market in the Shanghai Stock Exchange despite the lockdown. The eight other sectors performed at par with the general market. This confirmation of the counter-cyclical nature of the health care and information technology sectors during the lockdown, which enabled them to overcome the pandemic outbreak, provides valuable insights with which investors can adjust their portfolios in similar situations in the future in a timely manner.

Like a Moth to a Flame: Does the Stock Market Exacerbate Credit Risks of Peer-to-Peer (P2P) Lending?

In this paper, we establish a causal link between fluctuations in the stock market and credit risks from the P2P lending market, by exploring information of more than 450 thousand loans on Renrendai.com, a leading Chinese P2P crowd lending platform. Based on the fact that retail investors exhibit a disproportional level of attention when the Shanghai Stock Exchange (SSE) composite index is above the 3,500 threshold, we find that both the default rate and the degree of delinquency have a disproportional jump for P2P loans borrowed above the 3,500 threshold of the SSE composite index. This effect is more pronounced when the loan quality is lower and when investors are more overconfident. Furthermore, when the SSE composite index exceeds 3,500, the overall creditworthiness of borrowers disproportionally deteriorates, indicated by higher interest rates, longer fulfill time needed to achieve the target loan amount, lower credit scores, and lower loan amounts. Overall, our results suggest that FinTech developments could amplify financial risks and hamper financial stability.

Financial asset yield series forecasting based on risk-neutral fuzzy bilinear regression and probabilistic neural network

Application of quantitative methods for forecasting purposes in financial markets has attracted significant attention from researchers and managers in recent years when conventional time series forecasting models can hardly develop the inherent rules of complex nonlinear dynamic financial systems. In this paper, based on the fuzzy technique integrated with the statistical tools and artificial neural network, a new hybrid forecasting system consisting of three stages is constructed to exhibit effectively improved forecasting accuracy of financial asset price. The sum of squared errors is minimized to determine the coefficients in fitting the fuzzy autoregression model stage for formulating sample groups to deal with data containing outliers. Fuzzy bilinear regression model introducing risk view based on quadratic programming algorithm that reflects the properties of both least squares and possibility approaches without expert knowledge is developed in the second stage. The main idea of the model considers the sub-models tracking the possible relations between the spread and the center, also linking the estimation deviation with risk degree of fitness of the model. In the third stage, fuzzy bilinear regression forecasting combining with the optimal architecture of probabilistic neural network classifiers indicates that the proposed method has great contribution to control over-wide interval financial data with a certain confidence level. Statistical validation and performance analysis using historical financial asset yield series on Shanghai Stock Exchange composite index all exhibit the effectiveness and stability of the proposed hybrid forecasting formulation compared with other forecasting methods.

Gold and Government Bonds as Safe-Haven Assets Against Stock Market Turbulence in China

We examine whether gold and China’s government bonds are safe-haven assets against the turbulence of the Shanghai Stock Exchange Composite Index by employing vine copula models during the 2003 to 2015 period. We find that either bonds or gold can be a weak safe haven but only gold can be a strong safe haven. Our simultaneous analysis advises against a joint safe-haven strategy of gold and bonds, given the high- to low-tail correlation. This result highlights an investment strategy of using a single safe-haven asset against the Chinese stock market turbulences.

Forecasting of stock price index using support vector regression with multivariate empirical mode decomposition

PurposeDespite the widespread use of univariate empirical mode decomposition (EMD) in financial market forecasting, the application of multivariate empirical mode decomposition (MEMD) has not been fully investigated. The purpose of this study is to forecast the stock price index more accurately, relying on the capability of MEMD in modeling the dependency between relevant variables.Design/methodology/approachQuantitative and comprehensive assessments were carried out to compare the performance of some selected models. Data for the assessments were collected from three major stock exchanges, namely, the standard and poor 500 index from the USA, the Hang Seng index from Hong Kong and the Shanghai Stock Exchange composite index from China. MEMD-based support vector regression (SVR) was used as the modeling framework, where MEMD was first introduced to simultaneously decompose the relevant covariates, including the opening price, the highest price, the lowest price, the closing price and the trading volume of a stock price index. Then, SVR was used to set up forecasting models for each component decomposed and another SVR model was used to generate the final forecast based on the forecasts of each component. This paper named this the MEMD-SVR-SVR model.FindingsThe results show that the MEMD-based modeling framework outperforms other selected competing models. As per the models using MEMD, the MEMD-SVR-SVR model excels in terms of prediction accuracy across the various data sets.Originality/valueThis research extends the literature of EMD-based univariate models by considering the scenario of multiple variables for improving forecasting accuracy and simplifying computability, which contributes to the analytics pool for the financial analysis community.

Portfolio Learning Based on Deep Learning

Traditional portfolio theory divides stocks into different categories using indicators such as industry, market value, and liquidity, and then selects representative stocks according to them. In this paper, we propose a novel portfolio learning approach based on deep learning and apply it to China’s stock market. Specifically, this method is based on the similarity of deep features extracted from candlestick charts. First, we obtained whole stock information from Tushare, a professional financial data interface. These raw time series data are then plotted into candlestick charts to make an image dataset for studying the stock market. Next, the method extracts high-dimensional features from candlestick charts through an autoencoder. After that, K-means is used to cluster these high-dimensional features. Finally, we choose one stock from each category according to the Sharpe ratio and a low-risk, high-return portfolio is obtained. Extensive experiments are conducted on stocks in the Chinese stock market for evaluation. The results demonstrate that the proposed portfolio outperforms the market’s leading funds and the Shanghai Stock Exchange Composite Index (SSE Index) in a number of metrics.

BLACK SWAN EVENT: AN EVIDENCE FROM CHINA’S ECONOMICS EFECTS

The prognosis of upcoming crises and the course of actually understanding them is increasingly becoming a major subject of discussions in pursuit of reliable indicators. The trade war between the United States and China, along with the COVID-19 pandemic are two events that took place in the Chinese economy with the aforementioned characteristics of the Black swan phenomenon, to which this latest professional analysis is devoted. The objective of this research is to examine the response of the Shanghai Stock Exchange Composite (SSEC) index, in addition to its relation with macroeconomic variables contributing towards a possible Black Swan Event. We employ an econometric methodology comprising of a unit root test, descriptive statistics, linear regression and correlation analysis for the period 2007-2019. Our results illustarte that the bubble from 2015, which is classified as a Black Swan event by many researchers, has a negative influence on the SSEC index. We can further deduce that there were some psychological effects on the Chinese stock market that lead to both, positive and negative trends of SSEC indices. The main findings confirmed that the Consumer Price Index, Exchange Rate, Interest Rate, Unemployment, GDP and Trade Balance were significantly elaborative macroeconomic variables, that had a substantial impact on the SSEC index.

A Hybrid Approach Integrating Multiple ICEEMDANs, WOA, and RVFL Networks for Economic and Financial Time Series Forecasting

The fluctuations of economic and financial time series are influenced by various kinds of factors and usually demonstrate strong nonstationary and high complexity. Therefore, accurately forecasting economic and financial time series is always a challenging research topic. In this study, a novel multidecomposition and self-optimizing hybrid approach integrating multiple improved complete ensemble empirical mode decompositions with adaptive noise (ICEEMDANs), whale optimization algorithm (WOA), and random vector functional link (RVFL) neural networks, namely, MICEEMDAN-WOA-RVFL, is developed to predict economic and financial time series. First, we employ ICEEMDAN with random parameters to separate the original time series into a group of comparatively simple subseries multiple times. Second, we construct RVFL networks to individually forecast each subseries. Considering the complex parameter settings of RVFL networks, we utilize WOA to search the optimal parameters for RVFL networks simultaneously. Then, we aggregate the prediction results of individual decomposed subseries as the prediction results of each decomposition, respectively, and finally integrate these prediction results of all the decompositions as the final ensemble prediction results. The proposed MICEEMDAN-WOA-RVFL remarkably outperforms the compared single and ensemble benchmark models in terms of forecasting accuracy and stability, as demonstrated by the experiments conducted using various economic and financial time series, including West Texas Intermediate (WTI) crude oil prices, US dollar/Euro foreign exchange rate (USD/EUR), US industrial production (IP), and Shanghai stock exchange composite index (SSEC).