scholarly journals CHINA’S MARKET AND GLOBAL ECONOMIC FACTORS

2018 ◽  
Vol 6 ◽  
pp. 58-61
Author(s):  
Mária Bohdalová ◽  
Michal Greguš
Keyword(s):  
Stock Returns ◽  
Stock Markets ◽  
Stock Exchange ◽  
Causal Relation ◽  
Composite Index ◽  
Causal Link ◽  
Economic Factors ◽  
Chinese Stock Market ◽  
Global Risk ◽  
Chinese Market

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.

scholarly journals EMPIRICAL ANALYSIS OF STOCK RETURNS AND VOLATILITY: EVIDENCE FROM ASIAN STOCK MARKETS

2016 ◽  
Vol 22 (6) ◽  
pp. 808-829 ◽  
Author(s):  
Nawaz AHMAD ◽  
Rizwan RAHEEM AHMED ◽  
Jolita VVEINHARDT ◽  
Dalia STREIMIKIENE
Keyword(s):  
Hong Kong ◽  
Stock Returns ◽  
Stock Markets ◽  
Graphical Representation ◽  
Stock Exchange ◽  
Causal Relation ◽  
Preliminary Examination ◽  
Long Run ◽  
Daily Data ◽  
Time Period

The objective of this research isto measure and examine volatilities among important stock markets of Asia and to ascertain a causal relation between volatility and stock returns. For this purpose six markets KSE100 (Karachi, Pakistan), BSE Sensex (Mumbai, India), NIKKEI 225 (Tokyo, Japan), Hang Seng (Hong Kong), Shanghai Stock Exchange (SSE) (Shanghai, China) and KOSPI (Seoul, South Korea) were considered. Stock market indices comprise of daily data from the period January 2002 to December 2009. The graphical representation of time series shows the preliminary examination of stock behaviors. The analysis shows the high correlation and heteroskedastic trend (volatility) among the stock markets in selected time period. After preliminary analysis the formal descriptive method of mean, standard deviation and coefficient of variation have been applied for measuring and ranking purposes. The results show that KOSPI has the highest average annual return of 12.67% and followed by BSE with 11.61%, whereas, KSE 100 has the least annual average returns of 9.31%. The highest volatility coefficient of 3.097 has been observed in Hang Seng (Hong Kong) followed by 2.87 in Nikkei (Tokyo). However, the KSE 100 observed the lowest volatility coefficient of 2.078. Bartlett’s test is applied for the inferential analysis to investigate whether the equality of volatility is the same in each market return. Finally, GARCH (1, 1) model is applied which concludes a significant ARCH (1) and GARCH (1) effects and confirms all markets’ returns are statistically significant since p < 0.01 and their Long Run Average Variances (LRAV) range from 1.52% to 2.54% for KSE100 Index and Shanghai Stock Exchange respectively.

scholarly journals 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

Data ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 26 ◽  
Author(s):  
Xiaoping Du ◽  
Lelai Deng
Keyword(s):  
Active Learning ◽  
Comprehensive Evaluation ◽  
Stock Exchange ◽  
Core Competencies ◽  
Composite Index ◽  
Chinese Stock Market ◽  
Chinese Market ◽  
Equity Financing ◽  
Market Makers ◽  
Medium Enterprises

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.

scholarly journals Stock Market Volatility Using GARCH Models: Evidence from South Africa and China Stock Markets

2017 ◽  
Vol 8 (6(J)) ◽  
pp. 237-245
Author(s):  
Priviledge Cheteni
Keyword(s):  
South Africa ◽  
Stock Returns ◽  
Stock Markets ◽  
Stock Exchange ◽  
Composite Index ◽  
Stock Market Volatility ◽  
Johannesburg Stock Exchange ◽  
Arch Effect ◽  
Show Evidence ◽  
High Volatility

Abstract: This study looks into the relationship between stock returns and volatility in South Africa and China stock markets. A Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is used to estimate volatility of the stock returns, namely, the Johannesburg Stock Exchange FTSE/JSE Albi index and the Shanghai Stock Exchange Composite Index. The sample period is from January 1998 to October 2014. Empirical results show evidence of high volatility in both the JSE market, and the Shanghai Stock Exchange. Furthermore, the analysis reveals that volatility is persistent in both exchange markets and resembles the same movement in returns. Consistent with most stock return studies, we find that movements of both markets seem to take a similar trajectory.Keywords: GARCH, ARCH effect, JSE index, Shanghai Stock Exchange Composite Index

scholarly journals Market coupling: an empirical study of the Sino-Korean game industry

2020 ◽  
Vol 17 (1) ◽  
pp. 291-303
Author(s):  
Jung Woon Park ◽  
Seungho Baek ◽  
Mina Glambosky ◽  
Seok Hee Oh
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Price ◽  
Composite Index ◽  
Chinese Stock Market ◽  
Chinese Market ◽  
Korean Economy ◽  
Game Industry ◽  
Korean Market ◽  
The Impact

This study aims to examine the relationship between the Korean and Chinese game industries, and more broadly, the Chinese stock market. Chinese firms are the most important partners and investors in the Korean game industry, which has emerged as a significant component of a thriving Korean economy. The paper examines the impact of growth in the Chinese game industry on the Korean market and the correlation and cointegration between the stock returns of nineteen Korean game companies, the Chinese stock market, and Chinese game companies. A portfolio constructed from Korean game companies listed on the KOSPI and KOSDAQ is analyzed. Variation in the Shanghai Composite Index is shown to significantly influence the performance of Korean game companies. Further, the Korean game industry is sensitive to changes in the stock price of leading Chinese game publishers. The Korean game industry returns more closely mirror the returns of the Chinese stock markets rather than the Korean markets, evidence of the influence of China. As growth and returns in the Korean game industry are closely related to the performance of the Chinese market, future performance is subject to political and economic changes in China.

scholarly journals Portfolio Learning Based on Deep Learning

2020 ◽  
Vol 12 (11) ◽  
pp. 202
Author(s):  
Wei Pan ◽  
Jide Li ◽  
Xiaoqiang Li
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Time Series Data ◽  
Stock Exchange ◽  
Composite Index ◽  
Series Data ◽  
High Dimensional ◽  
Chinese Stock Market ◽  
High Return ◽  
Stock Information

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.

Is disposition related to momentum in Chinese market?

2015 ◽  
Vol 41 (6) ◽  
pp. 600-614 ◽  
Author(s):  
Liu Liu Kong ◽  
Min Bai ◽  
Peiming Wang
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Disposition Effect ◽  
Chinese Stock Market ◽  
Momentum Effect ◽  
Chinese Market ◽  
Firm Level ◽  
Content Type ◽  
Level Data ◽  
The Relationship

Purpose – The purpose of this paper is to examine whether the framework of Prospect Theory and Mental Accounting proposed by Grinblatt and Han (2005) can be applied to analyzing the relationship between the disposition effect and momentum in the Chinese stock market. Design/methodology/approach – The paper applies the methodology proposed by Grinblatt and Han (2005). Findings – Using firm-level data, with a sample period from January 1998 to June 2013, the authors find evidence that the momentum effect in the Chinese stock market is not driven by the disposition effect, contradicting the findings of Grinblatt and Han (2005) concerning the US stock market. The discrepancies in the findings between the Chinese and US stock markets are robust and independent of sample periods. Research limitations/implications – The findings suggest that Grinblatt and Han’s model may not be applicable to the Chinese stock market. This is possibly because of the regulatory differences between the two stock markets and cross-national variation in investor behavior; in particular, the short-selling prohibition in the Chinese stock market and greater reference point adaptation to unrealized gains/losses among Chinese compared to Americans. Originality/value – This study provides evidence of the inapplicability of Grinblatt and Han’s model for the Chinese stock market, and shows the differences in the relationship between disposition effect and momentum between the Chinese and US stock markets.

scholarly journals Measuring Information Asymmetry in Large Active Firms on the Tehran Stock Exchange

2016 ◽  
Vol 63 (3) ◽  
pp. 333-346
Author(s):  
Mostafa Shamsoddini ◽  
Mohammad N. Shahiki Tash ◽  
Farhad Khodadad-Kashi
Keyword(s):  
Firm Performance ◽  
Financial Markets ◽  
Information Asymmetry ◽  
Stock Markets ◽  
Stock Exchange ◽  
Composite Index ◽  
Financial Information ◽  
Investment Strategies ◽  
Information Measuring ◽  
Informational Environment

In financial markets, transparency of financial information is one of the most effective variables of investment strategies. Information asymmetry can seriously affect firm performance on the stock exchange and firms with a poor informational environment can lose the interest of investors. Reducing information asymmetry can have an important effect on firm performance on the stock exchange. Firms may lack a clear informational environment in the market because of the emerging conditions governing the Tehran Stock Exchange. Because larger and more active firms on the Tehran Stock Exchange provide more information, measuring the informational environment of these firms provides an overview of information asymmetry. The present study calculated the information asymmetry in these firms using the PIN and FE indices. The inconsistent results provided by these indices prompted the authors to offer a new index that is a composite of the PIN and FE that can better explain information asymmetry in developing market such as Asian stock markets. The results show that the new composite index, by using the mechanisms of the PIN and FE indices, provides a better outcome. The new composite index shows that the Tosee Melli Inv (TMEL1), Mobarakeh Steel (FOLD1), Iran Mobil Tele (HMRZ1), Saipa (SIPA1) and I.N.C. Ind. (MSMI1) firms have a better informational environment on the Tehran Stock Exchange.

TIME-VARYING LONG MEMORIES OF THE CHINESE CURRENCY AND STOCK MARKETS BASED ON THE HURST EXPONENT

2014 ◽  
Vol 13 (01) ◽  
pp. 1450007 ◽  
Author(s):  
CAO GUANGXI ◽  
HAN YAN ◽  
CUI WEIJUN
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Stock Markets ◽  
Hurst Exponent ◽  
Composite Index ◽  
Fluctuation Analysis ◽  
Time Varying ◽  
Chinese Stock Market ◽  
Currency Market ◽  
Cointegration Tests

Based on the daily return and volatility series of the Chinese yuan (RMB)/US dollar (USD) exchange rate and the Shanghai Stock Composite Index, the time-varying long memories of the Chinese currency and stock markets are investigated by comprehensively using the rescaled range (R/S), the modified R/S, and the detrended fluctuation analysis methods. According to the results drawn: (1) the efficiency of the Chinese currency market has not improved significantly, whereas the efficiency of the Chinese stock market has improved steadily, (2) volatility series presents longer memory than return series either in the Chinese currency or stock market and (3) the time-varying Hurst exponent of the Chinese currency market is sensitive to the reform that enhances the flexibility of the RMB exchange rate. Moreover, we find that short-term bidirectional Granger causal relationship exists, but no long-run equilibrium relationship between the time-varying Hurst exponents of the Chinese currency and stock markets was found based on the Granger causality and cointegration tests, respectively.

scholarly journals The Relevance of Cboe Volatility Index to Stock Markets in Emerging Economies

2015 ◽  
Vol 6 (1) ◽  
pp. 93-106
Author(s):  
Tamara Mariničevaitė ◽  
Jovita Ražauskaitė
Keyword(s):  
Stock Market ◽  
Emerging Economies ◽  
Stock Markets ◽  
Chinese Stock Market ◽  
Stock Market Volatility ◽  
Global Risk ◽  
Stock Market Index ◽  
Market Returns ◽  
Stock Market Returns ◽  
Volatility Index

We examine the capability of CBOE S&P500 Volatility index (VIX) to determine returns of emerging stock market indices as compared to local stock markets volatility indicators. Our study considers CBOE S&P500 VIX, local BRIC stock market volatility indices and BRIC stock market MSCI indices daily returns in the period from January 1, 2009 to September 30, 2014. Research is conducted in two steps. First, we perform Spearman correlation analysis between daily changes in CBOE S&P500 VIX, local BRIC stock market VIX and MSCI BRIC stock market indices returns. Second, we perform multiple regression analysis with ARCH effects to estimate the relevance of CBOE S&P500 VIX and local VIX in determining BRIC stock market returns. Research reports weak correlation between CBOE S&P500 VIX and local VIX (except for Brazil). Furthermore, results challenge the assumption of CBOE S&P500 VIX being an indicator of global risk aversion. We conclude that commonly documented trends of rising globalization and stock markets co-integration are not yet present in emerging economies, therefore the usage of CBOE S&P500 VIX alone in determining BRIC stock market returns should be considered cautiously, and local volatility indices should be accounted for in analysis. Furthermore, the data confirms the presence of safe haven properties in Chinese stock market index.

scholarly journals Chinese stock market sectoral indices performance in the time of novel coronavirus pandemic

2020 ◽  
Author(s):  
Venus Khim-Sen Liew ◽  
Chin-Hong Puah
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Descriptive Analysis ◽  
Composite Index ◽  
Chinese Stock Market ◽  
Health Care Information ◽  
Stock Market Performance ◽  
Health Care Information Technology ◽  
Novel Coronavirus ◽  
Regulatory Commission

Abstract 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 sectorial 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.

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