Shanghai Stock Exchange Composite Index Research Articles

An empirical study of the self-fulfilling prophecy effect in Chinese stock market

We analyzed data collected from retail investors in the Chinese stock market from a Fintech mobile platform to find evidence of the self-fulfilling prophecy effect. We found a statistically significant correlation between the predicted and actual Shanghai Stock Exchange Composite Index (SSECI) as well as non-random deviation patterns. We also analyzed participating investor behaviors and discussed the implications and future research.

Forecasting realized variance using asymmetric HAR model with time-varying coefficients

This paper proposes an asymmetric HAR model with time-varying coefficients (TVC-AHAR) for modeling and forecasting realized variance. The TVC-AHAR model includes good and bad volatilities and assumes the associated time-varying coefficients to be driven by a latent Gaussian autoregressive process. The model is easy to estimate and implement by using maximum likelihood based on Kalman filter. Empirical analysis using two stock market indices of China, the Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index, shows that our proposed TVC-AHAR model yields more accurate out-of-sample forecasts of realized variance compared with the other models.

Efficiency and Multifractality Analysis of the Chinese Stock Market: Evidence from Stock Indices before and after the 2015 Stock Market Crash

This paper examines the daily return series of four main indices, including Shanghai Stock Exchange Composite Index (SSE), Shenzhen Stock Exchange Component Index (SZSE), Shanghai Shenzhen 300 Index (SHSE-SZSE300), and CSI Smallcap 500 index (CSI500) in Chinese stock market from 2000 to 2018 by multifractal detrended fluctuation analysis (MF-DFA). The series of the daily return of the indices exhibit significant multifractal properties on the whole time scale and SZSE has the highest multifractal properties among the four indices, indicating the lowest market efficiency. The multifractal properties of four indices are due to long-range correlation and fat-tail characteristics of the non-Gaussian probability density function, and these two factors have different effects on the multifractality of four indices. This paper aims to compare the multifractility degrees of the four indices in three sub-samples divided by the 2015 stock market crash and to discuss its effects on efficiency of the Shanghai and Shenzhen stock market in each sub-sample. Meanwhile, we study the effect of the 2015 stock market crash on market efficiency from the statistical and fractal perspectives, which has theoretical and practical significance in the application of Effective Market Hypothesis (EMH) in China’s stock market, and it thereby affects the healthy and sustainability of the market. The results also provide important implications for further study on the dynamic mechanism and efficiency in stock market and they are relevant to portfolio managers and policy makers in a number of ways to maintain the sustainable development of China’s capital market and economy.

Textual Sentiment of Chinese Microblog Toward the Stock Market

In this paper, text mining and statistical models are deployed to explore the relationship between the Shanghai Stock Exchange Composite Index (SSECI) and the collective emotions of individual investors. The emotions of individual investors are quantified by extracting and aggregating investor online posts that contain finance-related keywords. To identify a set of finance-related keywords, three years of blogs from a famous financial blog site are segmented by an automatic text segmentation method; meanwhile, in the literature of social media, people typically select keywords manually. Posts that discuss the keywords are extracted out of all types of topics from Sina Weibo, the largest microblog platform in China. Statistical results reveal the relationship between daily posts and daily opening prices with a one-day lag, which indicates the existence of information (news) propagation lag. This study contributes to the existing literature by demonstrating that the microblog sentiment level reports can be quantitatively incorporated as a proxy to provide valuable support to portfolio decision making.

Random Walks and Market Efficiency in Chinese and Indian Equity Markets

Hypothesis of Market Efficiency is an important concept for the investors across the globe holding diversified portfolios. With the world economy getting more integrated day by day, more people are investing in global emerging markets. This means that it is pertinent to understand the efficiency of these markets. This paper tests for market efficiency by studying the impact of global financial crisis of 2008 and the recent Chinese crisis of 2015 on stock market efficiency in emerging stock markets of China and India. The data for last 20 years was collected from both Bombay Stock Exchange (BSE200) and the Shanghai Stock Exchange Composite Index and divided into four sub-periods, i.e. before financial crisis period (period-I), during recession (period-II), after recession and before Chinese Crisis (periodIII) and from the start of Chinese crisis till date (period- IV). Daily returns for the SSE and BSE were examined and tested for randomness using a combination of auto correlation tests, runs tests and unit root tests (Augmented Dickey-Fuller) for the entire sample period and the four sub-periods. The evidence from all these tests supports that both the Indian and Chinese stock markets do not exhibit weak form of market efficiency. They do not follow random walk overall and in the first three periods (1996 till the 2015) implying that recession did not impact the markets to a great extent, although the efficiency in percentage terms seems to be increasing after the global financial crisis of 2008.

Stock Market Forecasting Using Restricted Gene Expression Programming.

Stock index prediction is considered as a difficult task in the past decade. In order to predict stock index accurately, this paper proposes a novel prediction method based on S-system model. Restricted gene expression programming (RGEP) is proposed to encode and optimize the structure of the S-system. A hybrid intelligent algorithm based on brain storm optimization (BSO) and particle swarm optimization (PSO) is proposed to optimize the parameters of the S-system model. Five real stock market prices such as Dow Jones Index, Hang Seng Index, NASDAQ Index, Shanghai Stock Exchange Composite Index, and SZSE Component Index are collected to validate the performance of our proposed method. Experiment results reveal that our method could perform better than deep recurrent neural network (DRNN), flexible neural tree (FNT), radial basis function (RBF), backpropagation (BP) neural network, and ARIMA for 1-week-ahead and 1-month-ahead stock prediction problems. And our proposed hybrid intelligent algorithm has faster convergence than PSO and BSO.

The empirical Bayes estimators of the mean and variance parameters of the normal distribution with a conjugate normal-inverse-gamma prior by the moment method and the MLE method

Most of the samples in the real world are from the normal distributions with unknown mean and variance, for which it is common to assume a conjugate normal-inverse-gamma prior. We calculate the empirical Bayes estimators of the mean and variance parameters of the normal distribution with a conjugate normal-inverse-gamma prior by the moment method and the Maximum Likelihood Estimation (MLE) method in two theorems. After that, we illustrate the two theorems for the monthly simple returns of the Shanghai Stock Exchange Composite Index.

Complex-Valued Ordinary Differential Equation Modeling for Time Series Identification

Time series identification is one of the key approaches to dealing with time series data and discovering the change rules. Therefore, time series forecasting can be treated as one of the most challenging issues in this field. In order to improve the forecasting performance, we propose a novel time series prediction model based on a complex-valued ordinary differential equation (CVODE) to predict time series. A multi expression programming (MEP) algorithm is utilized to optimize the structure of the CVODE model. So as to achieve the optimal complex-valued coefficients, a novel optimization algorithm based on a complex-valued crow search algorithm (CVCSA) is proposed. The chaotic Mackey-Glass time series, small-time scale traffic measurements, Nasdaq-100 index, and Shanghai stock exchange composite index are utilized to evaluate the performance of our method. The results prove that our proposed method could predict more accurately than state-of-the-art real-valued neural networks and an ordinary differential equation (ODE). The CVCSA has faster convergence speed and stronger optimization ability than the crow search algorithm (CSA) and particle swarm optimization (PSO).

Influences of Macroeconomic Variables on Stock Market in China: An Empirical Analysis

This paper investigates the influences of macroeconomic variables on the stock market in China. We use Granger causality tests, impulse response functions, and variance decompositions to examine how fundamental macroeconomic variables, such as output proxied by electricity generation, inflation, money supply, and short-term interest rate affect the Shanghai Stock Exchange Composite Index. Our estimation results indicate that variables that measure macroeconomic activity, such as output growth and inflation, have no statistically significant impact on stock returns. Moreover, the stock returns do not respond to changes of monetary policy variables such as money supply and short-term interbank offered rate. This implies that monetary policy does not exert significant influences on stock returns. In sum, the performance of the China stock market does not reflect macroeconomic fundamentals.

Investor's Anticipation and Future Market Movement: Evidence of Self-Fulfilling Prophecy Effect from the the Chinese Stock Market

We analyzed data collected from retail investors in the Chinese stock market from a Fintech mobile platform to find evidence of the self-fulfilling prophecy effect. We found a statistically significant correlation between the predicted and actual Shanghai Stock Exchange Composite Index (SSECI) as well as non-random deviation patterns. We also analyzed participating investor behaviors and discussed the implications and future research.

Variance Persistence in the Greater China Region: A Multivariate GARCH Approach

This paper utilizes three Multivariate General Autoregressive Conditional Heteroscedasticity (MGARCH) models to determine variance persistence in the Greater China region from 2009 to 2014. The first approach applies the Baba, Engle, Kraft and Kroner (BEKK) model and shows that the Shanghai Stock Exchange Composite Index (SSEI), Taiwan Capitalization Weighted Stock Index (TAEIX) and the Hang Seng Stock Index (HSEI) stock returns are all functions of their lagged covariances and lagged cross-product innovations. The second MGARCH approach applies two methodologies, namely, dynamic conditional correlation (DCC), and constant conditional correlation (CCC) estimations. The DCC model concludes both short- and long-run persistencies between Taiwan’s TAIEX and Hong Kong’s HSEI. Alternatively, the CCC model confirms the initial findings of the BEKK model, and adds that the relationships among these three strong economies are stable in the long-run. The log-likelihood values determine that the DCC model is better in judging volatility dynamics in the Greater China region, because of economic clauses brought by the Closer Economic Partnership Arrangement (CEPA), the Economic Co-operation Framework Agreement (ECFA) and the Hong Kong - Taiwan Business Cooperation Committee (BCC).

Linkage influence of energy market on financial market by multiscale complexity synchronization

Crude oil markets play an important role in the international economy, and shocks of crude oil prices have significant effects on various economic activities. In this paper, a novel definition of time delay based on multiscale composite complexity synchronization analysis is proposed in this work, and it is applied to investigate whether stock markets have delayed reaction to crude oil markets’ large volatility or not. The effect of crude oil energy markets on stock markets is investigated, and the data of West Texas Intermediate crude oil spot price, Europe Brent spot price, China Daqing spot price, Shanghai Stock Exchange Composite Index and Standard & Poor’s 500 Index are selected for the empirical research. Then, based on the multiscale time delay between crude oil markets and financial markets, the linkage synchronization and correlation relationship between crude oil markets and stock markets are measured by cross recurrence quantification analysis, and deep canonical correlation analysis which is firstly utilized in this field.

CHINA’S MARKET AND GLOBAL ECONOMIC FACTORS

The aim of this paper is to analyze the causal relation between the Chinese stock market and the US market. We investigate the dependence structures between two Chinese stock markets (Shanghai Stock Exchange Composite Index (SHCOMP) and Hong Kong Hang Seng Index (HSCEI) markets) and global economic factors such as SP 500 stock markets, volatility index VIX, crude oil and gold. We have used data based on a period from January 2000 to June 2017. The aim of this paper is to explore the causal link between the Chinese market and global economic factors. We have discovered asymmetric causal relations between stock returns and global risk factors based on a quantile regression.

EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction

Stock market forecasting is a vital component of financial systems. However, the stock prices are highly noisy and non-stationary due to the fact that stock markets are affected by a variety of factors. Predicting stock market trend is usually subject to big challenges. The goal of this paper is to introduce a new hybrid, end-to-end approach containing two stages, the Empirical Mode Decomposition and Factorization Machine based Neural Network (EMD2FNN), to predict the stock market trend. To illustrate the method, we apply EMD2FNN to predict the daily closing prices from the Shanghai Stock Exchange Composite (SSEC) index, the National Association of Securities Dealers Automated Quotations (NASDAQ) index and the Standard & Poor’s 500 Composite Stock Price Index (S&P 500), which respectively exhibit oscillatory, upward and downward patterns. The results are compared with predictions obtained by other methods, including the neural network (NN) model, the factorization machine based neural network (FNN) model, the empirical mode decomposition based neural network (EMD2NN) model and the wavelet de-noising-based back propagation (WDBP) neural network model. Under the same conditions, the experiments indicate that the proposed methods perform better than the other ones according to the metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, we compute the profitability with a simple long-short trading strategy to examine the trading performance of our models in the metrics of Average Annual Return (AAR), Maximum Drawdown (MD), Sharpe Ratio (SR) and AAR/MD. The performances in two different scenarios, when taking or not taking the transaction cost into consideration, are found economically significant.

Cascading Logistic Regression Onto Gradient Boosted Decision Trees to Predict Stock Market Changes Using Technical Analysis

In the data mining and machine learning fields, forecasting the direction of price change can be generally formulated as a supervised classfii cation. This paper attempts to predict the direction of daily changes of the Nasdaq Composite Index (NCI) and of the Standard & Poor's 500 Composite Stock Price Index (S&P 500) covering the period from January 3, 2012 to December 23, 2016, and of the Shanghai Stock Exchange Composite Index (SSEC) from January 4, 2010 to December 31, 2014. Due to the complexity of stock index data, we carefully combine raw price data and eleven technical indicators with a cascaded learning technique to improve the performance of the classifi cation. The proposed learning architecture LR2GBDT is obtained by cascading the logistic regression (LR) model onto the gradient boosted decision trees (GBDT) model. Given the same test conditions, the experimental results show that the LR2GBDT model performs better than the baseline LR and GBDT models for these stock indices, according to the performance metrics Hit ratio, Precision, Recall and F-measure. Furthermore, we use these models to develop simple trading strategies and assess their performance in terms of their Average Annual Return, Maximum Drawdown, Sharpe Ratio and Average Annualized Return/Maximum Drawdown. When transaction costs and buy-sell thresholds are taken into account, the best trading strategy derived from LR2GBDT model still reaches the highest Sharpe Ratio and clearly beats the buy-and-hold strategy. The performances are found to be both statistically and economically signi ficant.

Current Core Competencies Trend of Small to Medium Enterprises (SMEs) in China—A Concurrent Comprehensive Evaluation and Active Learning Study of Newly Listed Chinese Stocks from 2015 through 2017

With plenty of stocks newly listed in the Chinese stock market everyday, it becomes more and more important for managers and governess to examine the trend of core competencies for these companies. Since most companies of newly listed stocks are small to medium-sized enterprises, existing methods are not capable enough to evaluate their competitiveness. To provide an understanding for the trend of core competencies in the Chinese market, this article conducts a concurrent comprehensive evaluation and active learning methodology to analyze the newly listed stocks in SSE (Shanghai Stock Exchange Composite Index) and SZSE (Shenzhen Stock Exchange Component Index) from 2015 through 2017. There is an evidence that Number of Market Makers, Equity Financing Frequency and Executive Replacement Frequency are three main core competencies from 2015 through 2017. Authors contend that their findings in this paper question the quo of core competencies for small to medium-sized enterprises in the Chinese market.

Dynamic impact of China's stock market on the international commodity market

It is generally believed that the international commodity markets’ downturn is attributed to China's stock markets collapse. To verify this, this paper assesses the influence of Shanghai (securities) stock exchange composite (SSEC) index on S&P GSCI total returns, a leading commodity composite index from August 31, 2006, to September 19, 2016. We implement wavelet transform to extract the approximations of SSEC and GSCI. Furthermore, we use the state space model to determine the margin between SSEC and GSCI as well as display the margin dynamic evolution over time. The margin denotes the units of GSCI change when SSEC increases a unit, which can display China's stock (equity) influence (CEI) on the international commodity market. We found that CEI reached its peak in 2007, then experienced two steep declines and an immediate mild rebound, and eventually hovered at a historically low level. Moreover, each inflection point of CEI could correspond to a specific international economic event, which indicates that the change of CEI is influenced by exogenous variables, especially the absolute increment of GDP and the U.S. dollar index. The influence of China's stock market on the international commodity market is not as great as believed and the diversification benefit still exists for the construction of portfolio. In addition, the change of China's stock market influence is closely related to exogenous factors, which deserves more attention for policy making to avoid unnecessary trade and monetary friction owing to misjudgment.

A Forecasting Model Based on Multi-Valued Neutrosophic Sets and Two-Factor, Third-Order Fuzzy Fluctuation Logical Relationships

Making predictions according to historical values has long been regarded as common practice by many researchers. However, forecasting solely based on historical values could lead to inevitable over-complexity and uncertainty due to the uncertainties inside, and the random influence outside, of the data. Consequently, finding the inherent rules and patterns of a time series by eliminating disturbances without losing important details has long been a research hotspot. In this paper, we propose a novel forecasting model based on multi-valued neutrosophic sets to find fluctuation rules and patterns of a time series. The contributions of the proposed model are: (1) using a multi-valued neutrosophic set (MVNS) to describe the fluctuation patterns of a time series, the model could represent the fluctuation trend of up, equal, and down with degrees of truth, indeterminacy, and falsity which significantly preserve details of the historical values; (2) measuring the similarities of different fluctuation patterns by the Hamming distance could avoid the confusion caused by incomplete information from limited samples; and (3) introducing another related time series as a secondary factor to avoid warp and deviation in inferring inherent rules of historical values, which could lead to more comprehensive rules for further forecasting. To evaluate the performance of the model, we explored the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as the major factor we forecast, and the Dow Jones Index as the secondary factor to facilitate the predicting of the TAIEX. To show the universality of the model, we applied the proposed model to forecast the Shanghai Stock Exchange Composite Index (SHSECI) as well.

Frequency-Division Combination Forecasting of Stock Market Based on Wavelet Multiresolution Analysis

Put forward a novel combination forecasting method (M-ARIMA-BP) that could make a more accurate and concise prediction of stock market based on wavelet multiresolution analysis. This innovative method operated by parsing of the low-frequency trend series and the high-frequency volatility series of stock market and gives an insight into the price series. Using the daily closing price data of SSE (Shanghai Stock Exchange) Composite Index and Shenzhen Component Index as samples, compared with conventional wavelet prediction model, ARIMA model, and BP neural network model, the empirical results show that the new algorithm M-ARIMA-BP can improve the accuracy of volatility forecasting and perform better in predicting prices rising and falling.

Predicting Stock Market Crashes in China

Predicting stock market crashes is extremely valuable for all investors. Several useful prediction models have been developed, focusing on mature financial markets, in North America, Europe, and Japan. The authors investigate whether traditional crash predictors—the price-to-earnings ratio (P/E), the cyclically adjusted price-to-earnings ratio (CAPE), and the bond–stock earnings yield differential model (BSEYD)—predict crashes for the Shanghai Stock Exchange Composite Index and the Shenzhen Stock Exchange Composite Index in mainland China. Using data from the early 1990s to the end of 2016, the authors find that the P/E ratio has predictive value for both exchanges over the entire period. When testing the P/E, CAPE, and BSEYD over a shorter nine-year period, the authors find that all measures had a higher predictive value for the Shenzhen index, where smaller, privately owned companies are listed, than for the Shanghai index, where larger, often state-owned enterprises trade.