Shanghai Stock Exchange Index Research Articles

Investor behavior and filter rule revisiting

The filter rule is a popular investment strategy, but its effectiveness and rationality are still controversial. We consider the process of the maximum stock price and the short loss aversion in the utility function and establish an optimal stopping time model. Our model describes the decision-making process of investors applying the filter rule strategy. The filter size of our model is dynamic and depends both on the characteristics of the price process and the investor utility function. A series of numerical simulations present the optimal times to sell the stock under different situations. We also conduct empirical analyses on the Shanghai Stock Exchange Index and the S&P 500 Index, and prove that the filter rule is effective.

The improved grasshopper optimization algorithm and its applications

Grasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.

Stock market index prediction based on reservoir computing models

Prediction of the financial market price is critical for financial decision-making and market policy-making. Recently, various machine learning and deep learning methods have been adopted to predict financial markets’ movements using historical time series of prices. However, accurate prediction of financial prices is still a long-standing challenge that always calls for new approaches. In this study, a novel machine learning model of reservoir computing is developed to predict stock market indices. The performance of the proposed new model is systematically evaluated using the time series of daily closing prices of seven major international stock market indices including S&P500 Index, New York Stock Exchange Composite, Dow Jones Industrial Average, Nasdaq Composite Index, Financial Times Stock Exchange 100 Index, Nikkei 225 Index, and Shanghai Stock Exchange Index between January 4, 2010, and December 31, 2018 covering 2,272 trading days. The results show that our model outperforms the widely used deep learning methods of long short-term memory and recurrent neural network in most cases. To further evaluate the predictive capability of our model, we compare our model to the other two newly reported deep learning methods in recent studies. Comparative results also show that our model is competitive to those deep learning methods in predicting stock market indices. Our study contributes to the literature by developing novel reservoir computing models for financial market predictions. Meanwhile, our results also provide practical implications for financial practitioners of potential financial applications of reservoir computing in financial time series analysis and predictions.

Empirical Analysis of Shanghai Stock Exchange Index Based on ARMIA Model and Neural Network Model

Stocks are an important part of the national economy. With the increase of liquidity in people's hands, more and more people choose to enter the stock market. In stock investment, accurate prediction of stock price index is not only of great significance to investors, but also of promoting the development of my country's stock market. It even has an important role in accelerating my country's economic development. The paper chose the ARIMA method based on linear technology for time series forecasting and the NN model that is good at mining the implicit nonlinear relationship in the data to compare the China Sea Securities Composite Index from January 21, 2020 to December 31, 2020. Empirical analysis of closing prices and short-term forecasts are made.

DYNAMIC VOLATILITY SPILLOVERS ACROSS THE EXTENDED GREATER CHINA REGION STOCK MARKETS

The study investigates volatility transmissions of major financial market indices found in the Extended Greater China Region (EGCR): the Shanghai Stock Exchange Index (SSEI), the Hang Seng Stock Exchange Index (HSEI), the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), and the Singapore Stock Exchange Index (STI). This paper utilizes three Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models to determine variance persistence in the EGCR. The MGARCH approach applies the Baba–Engle–Kraft–Kroner (BEKK) model and the dynamic conditional correlation (DCC) and constant conditional correlation (CCC) estimations. This research found that the SSEI consistently has the highest volatility among the stock markets in the EGCR, and this can be explained by the poor acknowledgment of minority shareholders’ rights or having a weak so-called “common-law” regime. The paper also found that volatilities in the EGCR are determined strongly by their own lagged values more than the product of lagged cross-products of shocks. This means that financial market movements in the EGCR are still strongly influenced by internal factors more than the external influences. The paper contributes to the literature by understanding the potential changes in volatility relationships in low- and high-volatility regimes of the EGCR, which can be used to improve risk management practices and asset allocation techniques in the region.

Research on Application of Multifractal Spectrum and Neural Network in Financial Market

With the deepening of the financial market’s reform and innovation, to better explain the market's stock price fluctuations, this article uses the traditional efficient market hypothesis to study further. This study is showed that the Shanghai Stock Exchange Index, the Shenzhen Stock Exchange Index, and the Nasdaq Index have the characteristics of "tip fat tail ", which indicates that the distribution is not normal. Based on the theory of multifractal spectrum, empirical analysis and comparison results of multifractal characteristics, three indexes are carried out to analyze the impact of the maturity of different financial markets on stock price fluctuations. Finally, the multifractal spectrum parameters and daily return rate as input variables, an average accuracy rate of 99.436%, are obtained for the Shanghai Composite Index forecast for the next 30 days, using a 5-layer neural network model. It has particular practical significance for controlling and managing financial risks.

Research on Financial Market Risk Based on GARCH-M Model

Since 1970, with the gradual acceleration of economic globalization and the rapid development of information technology, the financial market has become increasingly unstable. Therefore, we must enhance our competitiveness in the financial market, enhance our ability to resist risks, and master effective measures such as measuring risks. In this paper, GARCH-M model and VAR method are used to study the value at risk of financial market and make an empirical analysis. Firstly, the VAR value calculation method based on GARCH-M model under generalized error distribution is given. Secondly, the closing price of Shanghai Stock Exchange Index is selected as sample data, and Eviews software is used to analyze its characteristics. The results show that the logarithmic yield series of the closing price of Shanghai Stock Exchange Index is not normally distributed, and the series has fluctuation aggregation effect, autocorrelation effect and heteroscedasticity effect. Finally, GARCH-M model is established, and VAR estimates at 95% and 99% confidence levels are calculated and tested. The results show that GARCH-M(1,1) model is more suitable for estimating the risk of logarithmic return rate of closing price of Shanghai Composite Index.

Experimental Evidence of Source Preference: Familiarity and Home Bias

This paper experimentally tests the Fox-Tversky (1995) source preference hypothesis as axiomatized in Chew and Sagi (2008) where people may have preference between equally distributed risks depending on the underlying sources of uncertainty. We study two forms of source preference. One is based on familiarity of risks arising from the trailing digit of stock prices in the home city exchange. Another is based on the trailing digit of the market index of the home city versus that of a foreign city. We find a familiarity-based source preference in portfolios comprising strictly dominated bets associated with more familiar stocks and in valuation of bets elicited using an ascending-price auction. Complementarily, we find a home bias in terms of Shanghai (Hong Kong) subjects preferring to bet on the trailing digit of the Shanghai Stock Exchange Index (Hang Seng Index) even though the same bets based on Dow Jones Industrial Average would pay more. Taken together, our study suggests that home bias is driven mainly by source preference rather than ambiguity aversion.

Impact of Macroeconomic Variabel and Global Indices on Islamic Stock Index: The Case Indonesia

Introduction to The Problem: The movement of the Islamic stock index can be influenced by changes in domestic macroeconomic conditions, not only domestic macroeconomics but also influenced by the stock markets of other countries.Purpose/Objective Study: The main objective of this study is to analyze the influence of the domestic macroeconomic and global stock indices on the Indonesian sharia stock index.Design/Methodology/Approach: This study uses a quantitative methodology with secondary data. The data sample method is saturated sample that all members of the population are used to be the research sample. The type of research data is monthly time series with a time period from May 2011 - July 2019, the selection of the Vactor Error Correction Model (VECM) research method based on the stationarity of the data on the first difference and the existence of cointegration models.Findings: The results showed in the short term all variables did not show a significant effect. In the long run, interest rates have a negative effect, while the exchange rate shows a positive effect on the movement of Islamic stock price indexes. Global stock indices such as the Shanghai Stock Exchange Index show a negative effect, and the Standard & Poor's 500 index shows a positive effect. While the Nikkei 225 index did not show a significant effect on the Indonesian Islamic stock index.

Analisis Pengaruh Inflasi, Nilai Tukar Rupiah, BI Rate, Jumlah Uang Beredar, dan Indeks Shanghai Stock Exchange terhadap Indeks Sri Kehati di Bursa Efek Indonesia Periode 2014-2019

This study is to explain the analysis of the influence of inflation, exchange rates, BI Rate, money supply, and Shanghai Stock Exchange index toward the Sri Kehati index on the Indonesia Stock Exchange (BEI) for the period 2014-2019. Data were analyzed by using multiple linear analysis techniques and use SPSS 18 as statistical analysis software. The sample type used is saturation samples so that there are 72 data taken monthly from 2014-2019. The results of this study indicate that inflation and exchange rate have a significant negative relationship to the Sri Kehati index. Meanwhile, the Shanghai Stock Exchange index has a significant positive relationship with the Sri Kehati index. The rest variable BI rate and money supply have no significant relationship to the Sri Kehati index. All independent variables simultaneously affect the Sri Kehati index. Based on the calculation the Adjusted R square value is 0.279 which means that the movement of the Sri Kehati index is influenced by 28% by the variables in this study and 72% is explained by other variables outside this study. Therefore, further research is expected to add other potential independent variables. The implication from this research is that investors should pay more attention to the exchange rate, inflation, and the Shanghai Stock Exchange index before investing in the Sri Kehati index.

Time series prediction based on deep confidence network

The timing deep belief network model (T-DBN) is proposed to solve the problems of gradient dispersion, local minimum, poor long-term prediction performance of nonlinear time series prediction and high complexity of high-dimensional sequence data in traditional computer neural networks. In the pre-training stage, the improved greedy pre-training algorithm is adopted. In the pre-training process, the gradient fixing parallel tempering (GFPT) algorithm is used to determine the depth of network by reconstruction error. In the stage of reverse adjustment, the quasi-newton method (BFGS) is adopted to obtain more accurate prediction performance. Combined with the theory of phase space reconstruction and BP (back propagation) neural network, the total power of agricultural machinery in Jiangxi province of China from 2016 to 2020 is predicted. For the highly nonlinear stock data, the characteristics of the Shanghai stock exchange index from 1990-12-20 to 2018-03-30 are extracted from the stock data of Tonghuashun software. T-DBN, DBN and long short-term memory (LSTM) models areused for stock forecasting with the prediction accuracy of 79.3%, 77.9% and 74.6%, respectively. The experimental results demonstrate the better prediction performance of T-DBN model in comparison with DBN and LSTM models.

The empirical research of ARMA-GARCH models based on high frequency data

Based on high frequency data (5 minutes) and low frequency data (day), this paper models and analyses the Shanghai Stock Exchange Index (SH1A0001). Through the stationarity test and the ARCH effect test, the ARMA (1,1) – GARCH (1,1) model is established. Under the assumption of the error term obeys the Laplace distribution, the estimation is obtained. By the result of forward prediction, it is found that the result of model prediction in high frequency data is better than those in low frequency data.

Short-term Forecast of Shanghai Composite Index in China Using BP Neural Network

This article analyses the historical statistics of the Shanghai Composite Index over the last 25 years, namely the characteristics and influencing factors. The article also predicts the trend of a 60-day time sequence of Shanghai composite index after 30th, Nov, 2018, using BP neural network model. The BP model fits the historical statistics well after consecutive training. The prediction result of the BP neural model indicates three characteristics of the trend in next 60 days: (1) the price will go up; (2) fluctuation frequency will reduce; (3) fluctuation range of the price will remain low.By analysing the relevant daily data of Shanghai Composite Index from 1993 to 2018, it is found that the forecast for Stock Market is very challengeable due to the intrinsic indeterminacy of the stock market and the complexity of the variables. To train and test the BP neural network model, this research adopts the trading record of 4465 transaction records as the training group, the next 935 trading days as the testing group and 935 trading days as verification group. The input variable in this BP neural network is Shanghai Composite Index (SZCindex), and the expected Shanghai Composite Index (EXSZCindex) is output variable. The BP neural network model is constructed as a three-layer neural network model.In the analysis of the daily closing price of the Shanghai Stock Exchange Index, this paper uses the goodness of fit to evaluate the BP neural network model and to continuously adjust the weight of the parameters. The results show that the training goodness of fit is 0.99898, and the correction goodness is 0.99897. The test goodness is 0.99904, and the overall goodness of fit is 0.99899, which indicates that the BP neural network has a rather good fitting effect. It is predicted that the price of the next 60 days from November 30, 2018 will rise to the highest point of 2623.874.In the end, the rationality of the predicted results is analysed in this paper. Via the analysis of the obtained data, it is found that there is no over-fit. The images of predicted values and real values are drawn by using the data from subjective and objective analysis.

ІНДИКАТОРИ РОЗВИТКУ ФОНДОВИХ РИНКІВ ТА ВИКЛИКИ ДЛЯ УКРАЇНИ

Стаття присвячена аналізу основних світових індикаторів кон’юнктури фондових ринків у взаємозв’язку із сучасними особливостями розвитку фондового ринку України. За результатами аналізу провідних фондових індексів країн виявлені сучасні тенденції в розвитку світових фондових ринків і фондового ринку України на сучасному етапі. Встановлено, що в умовах змінного сучасного середовища існують виклики, які формують цінові аномалії в розвитку фондових ринків, та обґрунтовано можливості подальшого розвитку фондового ринку України

Impacts of lagged returns on the risk-return relationship of Chinese aggregate stock market: Evidence from different data frequencies

In this paper we investigate if lagged returns have impacts on the relationship between the conditional mean and variance of Chinese aggregate stock market. We consider different frequent data and the influence of lagged returns on both expected returns and risk-return tradeoff simultaneously. We investigate both Shanghai Stock Exchange Index and Shenzhen Stock Exchange Index and conduct both in-sample analysis and out-of-sample analysis. The empirical findings suggest that the risk-return tradeoff is time-varying and affected by lagged returns, but the results are sensitive to different data frequencies. At daily data frequency, we found statistically significant negative impacts of negative lagged returns on future risk-aversion level but significant positive impacts on future expected returns. The out-of-sample return forecasting test suggest that the GARCH-in-Mean model with lagged returns can improve the forecasting accuracy compared to the GARCH-in-Mean model without lagged returns at all data frequencies. Lastly, applying the GARCH-in-Mean model with lagged returns to 10-day data can bring about evident utility improvement for risk-averse investors even when the trading cost is non-zero and risk-free rate is zero.

Deep Hierarchical Strategy Model for Multi-Source Driven Quantitative Investment

Using deep learning to maximize the benefits of a series of risks in the securities market was a very interesting and widely concerned problem. From the perspective of multi-source driving, we proposed a feature combination method based on prior knowledge and designed a deep hierarchical strategy model to solve this challenge. This model includes a pre-judging module, which implements trend judgment of the time series, and an operation module that performs the trading actions. For the pre-judging module, we designed a regression constraint Wasserstein generative adversarial network (WGAN) to complete the mission instead of the common discriminant methods in the past. As for the operation module, in order to make the machine learning more compatible with human learning characteristics and having the ability to control risks, we designed a network structure based on deep deterministic policy gradient (DDPG), which is one of the reinforcement learning algorithms to decide continuous trading position. We used the Dow Jones Industrial Index and the Shanghai Stock Exchange Index to conduct training and testing. The results show that the proposed model has a good performance in terms of return on investment and robustness.

Volatility Spillovers between the US and China Stock Markets: Structural Break Test with Symmetric and Asymmetric GARCH Approaches

The paper examines the short-run spillover effects of daily stock returns and volatilities between the Standard & Poor's (S&P) 500 stock index in the US and the Shanghai Stock Exchange (SSE) index in China. First, we find that a structural break occurred in the SSE stock return mean in December 2005. Second, by analyzing modified general autoregressive conditional heteroscedasticity (GARCH)(1,1)-M models, we find evidence of a symmetric and asymmetric volatility spillover effect from the US to the China stock market in the post-break period. Third, we observe the symmetric volatility spillover effect from China to the US in the post-break period.

Data Analysis and Statistical Behaviors of Stock Market Fluctuations

In this paper, the data of Chinese stock markets is analyzed by the statistical methods and computer sciences. The fluctuations of stock prices and trade volumes are investigated by the method of Zipf plot, where Zipf plot technique is frequently used in physics science. In the first part of the present paper, the data of stocks prices and trade volumes in Shanghai Stock Exchange and Shenzhen Stock Exchange is analyzed, the statistical behaviors of stocks prices and trade volumes are studied. We select the daily data for Chinese stock markets during the years 2002-2006, by analyzing the data, we discuss the statistical properties of fat tails phenomena and the power law distributions for the daily stocks prices and trade volumes. In the second part, we consider the fat ails phenomena and the power law distributions of Shanghai Stock Exchange Index and Shenzhen Stock Exchange Index during the years 2002-2007, and we also compare the distributions of these two indices with the corresponding distributions of the Zipf plot.

Taking the Temperature - Forecasting GDP Growth for Mainland China

We present a new composite leading indicator of economic activity in mainland China, estimated using a dynamic factor model. Our leading indicator is constructed from three series: exports, a real estate climate index, and the Shanghai Stock Exchange index. These series are found to share a common, unobservable element from which our indicator can be identified. This indicator is then incorporated into out-of-sample one-step-ahead forecasts of Chinese GDP growth. Recursive out-of-sample accuracy tests indicate that the small-scale factor model approach leads to a successful representation of the sample data and provides an appropriate tool for forecasting Chinese business conditions.

The Chinese stock exchange market: operations and efficiency

The Chinese stock market has developed rapidly since early 1990s, when the two stock exchanges, the Shanghai Securities Exchange and the Shenzhen Securities Exchange, were established. Until 2000, the number of listed domestic companies has reached over 1000, and market capitalization relative to GDP reached about 33.4%. As China joins WTO, the Chinese stock market will become a great concern of the global investors, and will play a more important role in the world economy. The purpose of this paper is to provide an up-to-data account of the Chinese stock exchange market and to test its efficiency. The daily data of the Shanghai Stock Exchange index and eight shares listed in the Shanghai Stock Exchanges are examined, for this purpose. The testing procedure involves three processes: (1) use the Durbin–Watson test, Durbin ‘h’ test, the Lagrange Multiplier test for autocorrelation to examine the assumption of the model that the successive occurrences are independent; (2) use the Dickey–Fuller tests for unit root to test the assumption that the occurrences are identically distributed; (3) use ARCH test to examine whether the residuals contain some hidden, possibly non-linear structure, and fit a GARCH-M(1,1) model to the first difference if the ARCH effect is found to be present in the share prices.