Dynamic causality between the U.S. stock market, the Chinese stock market and the global gold market: implications for individual investors’ diversification strategies

2019 ◽  
Vol 51 (43) ◽  
pp. 4742-4756
Author(s):  
Ganghua Mei ◽  
Robert McNown
Keyword(s):  
Stock Market ◽  
Individual Investors ◽  
Chinese Stock Market ◽  
Gold Market ◽  
Diversification Strategies ◽  
The U.S

scholarly journals Impact persistence of stock market risks in commodity markets: Evidence from China

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259308
Author(s):  
Shusheng Ding ◽  
Zhipan Yuan ◽  
Fan Chen ◽  
Xihan Xiong ◽  
Zheng Lu ◽  
...  
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Market Risk ◽  
Commodity Markets ◽  
Chinese Stock Market ◽  
Stock Market Volatility ◽  
Gold Market ◽  
Shock Impact ◽  
Market Shocks ◽  
The Impact

The risk spillover among financial markets has been noticeably investigated in a burgeoning number of literature. Given those doctrines, we scrutinize the impact persistence of volatility spillover and illiquidity spillover of Chinese commodity markets in this paper. Based on the sample from 2010 to 2020, we reveal that there is a cross-market spillover of volatility and illiquidity in China and also, interactions between volatility and illiquidity in different financial markets are pronounced. More importantly, we demonstrate that different commodity markets have different responsiveness to stock market shocks, which embeds their market characteristics. Specifically, we discover that the majority of the traders in gold market might be hedger and therefore gold market is more sensitive to stock market illiquidity shock and thus the shock impact in persistent. On the other hand, agricultural markets like corn and soybean markets might be dominated by investors and thus those markets respond to the stock market volatility shocks and the shock impact in persistent over 10 periods given the first period of risk shock happening. In fact, different Chinese commodity markets’ responsiveness towards Chinese stock market risk shocks indicates the stock market risk impact persistence in Chinese commodity markets. This result can help policymakers to understand the policy propagation effect according to this risk spillover channel and risk impact persistence mechanism in China.

iTrade

2015 ◽  
Vol 11 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Yong Hu ◽  
Xiangzhou Zhang ◽  
Bin Feng ◽  
Kang Xie ◽  
Mei Liu
Keyword(s):  
Stock Market ◽  
Large Scale ◽  
Concept Drift ◽  
Composite Index ◽  
Performance Comparison ◽  
Mobile App ◽  
Data Driven ◽  
Support Vector ◽  
Individual Investors ◽  
Chinese Stock Market

Among all investors in the Chinese stock market, more than 95% are non-professional individual investors. These individual investors are in great need of mobile apps that can provide professional and handy trading analysis and decision support everywhere. However, financial data is challenging to analyze because of its large-scale, non-linear and noisy characteristics in a varying stock environment. This paper develops a Mobile Data-Driven Stock Trading System (iTrade), which is a mobile app system based on Client-Server architecture and various data mining techniques. The iTrade is characterized by 1) a data-driven intelligent learning model, which can provide further insight compared to empirical technical analysis, 2) a concept drift adaptation process, which facilitates the model adaptation to market structure changes, and 3) a rigorous benchmark analysis, including the Buy-and-Hold strategy and the strategies of three world-famous master investors (e.g., Warren E. Buffett). Technologies used in iTrade include the Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, Support Vector Machine (SVM) and risk-adjusted portfolio optimization. An application case of iTrade is presented, which is based on a seven-year (2005-2011) back-testing. Evaluation results indicated that iTrade could gain much higher cumulative return compared to the benchmark (Shanghai Composite Index). To the best of our knowledge, this is the first study and mobile app system that emphasizes and investigates the concept drift phenomenon in stock market, as well as the performance comparison between data-driven intelligent model and strategies of master investors.

iTrade

2018 ◽  
pp. 995-1014
Author(s):  
Yong Hu ◽  
Xiangzhou Zhang ◽  
Bin Feng ◽  
Kang Xie ◽  
Mei Liu
Keyword(s):  
Stock Market ◽  
Concept Drift ◽  
Performance Comparison ◽  
Mobile App ◽  
Data Driven ◽  
Individual Investors ◽  
Chinese Stock Market ◽  
Back Testing ◽  
Structure Changes ◽  
Selection Operator

Among all investors in the Chinese stock market, more than 95% are non-professional individual investors. These individual investors are in great need of mobile apps that can provide professional and handy trading analysis and decision support everywhere. However, financial data is challenging to analyze because of its large-scale, non-linear and noisy characteristics in a varying stock environment. This paper develops a Mobile Data-Driven Stock Trading System (iTrade), which is a mobile app system based on Client-Server architecture and various data mining techniques. The iTrade is characterized by 1) a data-driven intelligent learning model, which can provide further insight compared to empirical technical analysis, 2) a concept drift adaptation process, which facilitates the model adaptation to market structure changes, and 3) a rigorous benchmark analysis, including the Buy-and-Hold strategy and the strategies of three world-famous master investors (e.g., Warren E. Buffett). Technologies used in iTrade include the Least Absolute Shrinkage and Selection Operator (Lasso) algorithm, Support Vector Machine (SVM) and risk-adjusted portfolio optimization. An application case of iTrade is presented, which is based on a seven-year (2005-2011) back-testing. Evaluation results indicated that iTrade could gain much higher cumulative return compared to the benchmark (Shanghai Composite Index). To the best of our knowledge, this is the first study and mobile app system that emphasizes and investigates the concept drift phenomenon in stock market, as well as the performance comparison between data-driven intelligent model and strategies of master investors.

scholarly journals Financial Technology: China’s Stock Markets vs U.S. Stock Markets

2021 ◽  
Vol 275 ◽  
pp. 01006
Author(s):  
Ruiqian Chang
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
The United States ◽  
Chinese Stock Market ◽  
Chinese Market ◽  
The Difference ◽  
The Many ◽  
Excessive Speculation ◽  
The U.S ◽  
Financial Technology

This paper provides a detailed analysis of the difference between the Chinese stock market and the U.S. stock market under the development of financial technology. In conclusion, we find that the Chinese stock market is more dominated by retail investors, but the United States owns more stocks, mostly held by institutional investors, and has a better financial mindset. The behavior of investors in the Chinese stock market is mainly the excessive speculation of investors in the Chinese market. This is one of the reasons for the many fluctuations in the Chinese stock market. Due to the speculative nature of China’s stock market, the floating ratio reflects the management mechanism of China’s stock market and helps to observe the correlation with the U.S. stock market. And technology and digitalization affect the trading of the stock market. This research is correlational, and there is no causality implied.

HOW TO MAKE A PROFITABLE TRADING STRATEGY MORE PROFITABLE?

2013 ◽  
Vol 58 (03) ◽  
pp. 1350019 ◽  
Author(s):  
TERENCE TAI-LEUNG CHONG ◽  
TAU-HING LAM
Keyword(s):  
Stock Market ◽  
Trading Strategy ◽  
Chinese Stock Market ◽  
Chinese Market ◽  
Trading Rules ◽  
New Strategy ◽  
Market Indexes ◽  
The U.S

Chong and Lam and Chong et al. show that SETAR(200) and MA(50) outperform other rules in both the U.S. and the Chinese stock market. This paper investigates the synergy of combining SETAR(200) and MA(50) rules in ten U.S. and Chinese stock market indexes. It is found that the SETAR rule performs better in the U.S. market, while the MA rule performs better in the Chinese market. In addition, we find evidence that a new strategy combining the two rules together is able to create synergy. An immediate implication of our result is that investors are able to improve the performance of their portfolios by combining existing profitable trading rules.

scholarly journals Analysis on the Psychology and Behavior of Individual Investors under the Influence of COVID-19

2020 ◽  
Vol 3 (5) ◽  
Author(s):  
Jialing Huang ◽  
Yixin He
Keyword(s):  
Stock Market ◽  
Investment Decisions ◽  
Individual Investors ◽  
Chinese Stock Market ◽  
Public Health Emergencies ◽  
History Of ◽  
The Individual ◽  
And Behavior ◽  
The Impact ◽  
Psychological Experiments

Due to the relatively short history of the development of the Chinese stock market, the investment philosophy and psychology of most individual investors are not particularly mature. Especially under the influence of public health emergencies, the individual investors' characteristic of the investment behavior in the stock market has become more obvious. This paper combines questionnaire and psychological experiments to study the factors that affect investment decisions of individual investors, and then takes COVID-19 as an example to analyze the impact of public emergencies on individual investors’ investment decisions in the stock market.

scholarly journals Half-day trading and spillovers

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Yifan Chen ◽  
Limin Yu ◽  
Jianhua Gang
Keyword(s):  
Stock Market ◽  
Spillover Effects ◽  
The United States ◽  
Time Varying ◽  
Chinese Stock Market ◽  
Conditional Correlation ◽  
Day Trading ◽  
Market Impacts ◽  
Positive Spillover ◽  
The U.S

AbstractThis paper investigates the linkage of returns and volatilities between the United States and Chinese stock markets from January 2010 to March 2020. We use the dynamic conditional correlation (DCC) and asymmetric Baba–Engle–Kraft–Kroner (BEKK) GARCH models to calculate the time-varying correlations of these two markets and examine the return and volatility spillover effects between these two markets. The empirical results show that there are only unidirectional return spillovers from the U.S. stock market to the Chinese stock market. The U.S. stock market has a consistently positive spillover to China’s next day’s morning trading, but its impact on China’s next day’s afternoon trading appears to be insignificant. This finding implies that information in the U.S. stock market impacts the performance of the Chinese stock market differently in distinct semi-day trading. Moreover, with respect to the volatility, there are significant bidirectional spillover effects between these two markets.

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