Co-movements between Shanghai Composite Index and some fund sectors in China

Wang, Jian; Shao, Wei; Ma, Chenmin; Chen, Wenbing; Kim, Junseok

doi:10.1016/j.physa.2021.125981

Cited by 8 publications

(6 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach can be used not only to predict future economic trends, but also to help policy makers make more informed decisions. For example, in the area of monetary policy, AI technology can simulate the effects of different monetary policy tools and thus optimize the central bank's decisions [2].…”

Section: Macro-economy and Stock Market Forecast 21 Macro-economymentioning

confidence: 99%

Prediction of Shanghai Composite Index Based on Macroeconomic Indicators and Artificial Intelligence Method

Lyu,

Zhu,

Lin

et al. 2023

Atlantis Highlights in Intelligent Systems

View full text Add to dashboard Cite

The stock market can be defined as a market that, on the one hand, facilitates companies that need financing and, on the other hand, provides opportunities for investors who need to invest. By predicting the rise and fall of stock indices, it can bring guidance to individuals and companies when to enter the financial market, and it can also provide theoretical implications for government economic policy making. However, the stock market is a complex system full of various information, it is not only affected by past information, but also by current political, economic and psychological factors, so it is difficult to accurately predict the rise and fall of the stock index. At present, the stock index rise and fall prediction methods are mainly applied technical analysis method and measurement time series analysis method, which applied technical method is used by more groups, because it almost does not need too much analysis but according to personal investment habits and experience, subjective color. The econometric time series method is a method that is effective only when used in an ideal situation, which requires the input of the independent variable indicators and the target variable is preferably linear, if it is a non-linear situation, the results will have no reference significance. In this paper, we combine the main capital flow model with support vector machine as a tool to construct a stock index up/down prediction scheme.

show abstract

Section: Macro-economy and Stock Market Forecast 21 Macro-economymentioning

confidence: 99%

Prediction of Shanghai Composite Index Based on Macroeconomic Indicators and Artificial Intelligence Method

Lyu,

Zhu,

Lin

et al. 2023

Atlantis Highlights in Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…4 This method can investigate the complex behaviors of autocorrelation and cross-correlation in financial markets, considering the multifractal characteristics of two non-stationary time series. Unlike the other multifractal cross-correlation analysis methods 5 The MFCCA algorithm enables to compute the arbitrary-order covariance function of two signals as well as the relative signs in the signals (Fan and Li, 2015;Gębarowski et al 2019;Li et al 2021;Wątorek et al 2019;Wang et al, 2021), thereby providing more reliable estimation of multiscale correlations between any two times series (Wątorek et al 2021). During crises, dynamic correlation analyses like MFCCA may provide additional insights into the safehaven role of gold against the stock market movements.…”

Section: Introductionmentioning

confidence: 99%

The impact of COVID-19 pandemic on the dynamic correlations between gold and U.S. equities: evidence from multifractal cross-correlation analysis

2022

View full text Add to dashboard Cite

This study exploits multifractal cross-correlation analysis (MFCCA) to investigate the impact of the COVID-19 pandemic on the cross-correlations between gold and U.S. equity markets using 1-min high-frequency data from January 1, 2019, to December 29, 2020. The MFCCA method shows that the pandemic caused an increase of multifractality in cross-correlations between the two markets. Specifically, the cross-correlations of small fluctuations became more persistent while those of large fluctuations became less persistent, explaining the source of multifractality. The findings of this study carry significant implications for investors, academicians, and policymakers. For example, the increase of multifractality of cross-correlation means that the non-linear relationship between gold and U.S. equity returns prevails more during economic downturns. Therefore, academicians may resort to non-linear techniques to evaluate the relationship between gold and U.S. equity markets during the health pandemic. Moreover, investors can know the value of hedging benefits over different investment time horizons during the pandemic. Finally, policymakers can better assess the economic downturns (i.e., those caused by health pandemics) over the dynamics of cross-correlation between gold and equity markets to make sound financial policies.

show abstract

“…Developed from MF-DFA, multifractal detrended cross-correlation analysis (MF-DCCA) (Zhou, 2008) has become well-known for exploring cross-correlation and multifractality between variables. For instance, Wang et al (2021) investigated the association between new energy sector and Shanghai Composite Index using MFCCA. They found that the persistence of cross-correlation of SSEC/new energy is weaker than SSEC/finance, but stronger than the SSEC/consumption and SSEC/medicine.…”

Section: Introductionmentioning

confidence: 99%

Market Efficiency and Cross-Correlations of Chinese New Energy Market with Other Assets: Evidence from Multifractality Analysis

Niu

Wang

2022

Comput Econ

View full text Add to dashboard Cite

Despite the upgrading of the attention and investment of new energy in Chinese public, its market efficiency and associations with other assets are relatively rarely explored. This paper, firstly, explores the multifractal feature and market efficiency of Chinese new energy market (NEI) by the multifractal detrended fluctuation analysis. Secondly, the multifractal cross-correlation analysis is performed to discuss the multifractality of cross-correlations between NEI and crude oil, external new energy indices (Global (SPGCE), United States (ECO) and Europe (ERIX)) and safe-haven asset (GOLD) respectively. The results show that Chinese new energy market has obvious multifractality with low market efficiency, which is mainly sourced from long-range correlation. It has the strongest linkages with external new energy markets and most insignificant association with gold. The heterogeneous sources contribute to their multifractal cross-correlations. It provides useful enlightenment for decision-makers to implement energy policy and reform, and for investors to make investment decisions.

show abstract

Co-movements between Shanghai Composite Index and some fund sectors in China

Cited by 8 publications

References 30 publications

Prediction of Shanghai Composite Index Based on Macroeconomic Indicators and Artificial Intelligence Method

Prediction of Shanghai Composite Index Based on Macroeconomic Indicators and Artificial Intelligence Method

The impact of COVID-19 pandemic on the dynamic correlations between gold and U.S. equities: evidence from multifractal cross-correlation analysis

Market Efficiency and Cross-Correlations of Chinese New Energy Market with Other Assets: Evidence from Multifractality Analysis

Contact Info

Product

Resources

About