Relationship between Herd Behavior and Chinese Stock Market Fluctuations during a Bullish Period Based on Complex Networks

2021 ◽  
pp. 1-17
Author(s):  
Yong Shi ◽  
Yuanchun Zheng ◽  
Kun Guo ◽  
Xinyue Ren
Keyword(s):  
Stock Market ◽  
Composite Index ◽  
Big Data Analysis ◽  
Mobile Internet ◽  
Investor Behavior ◽  
Herd Behavior ◽  
Chinese Stock Market ◽  
Pairwise Correlation ◽  
Market Sentiment ◽  
The Relationship

Herding has a great impact on stock market fluctuations, and it is possible for researchers to analyze the herding effect due to the recent popularity of mobile Internet and the development of big data analysis technology. In this paper, we propose both investor-based and stock-based sentiment propagation networks of Chinese stock markets based on the simple pairwise correlation of posts’ sentiment indexes. And the relationship between the herding effect and Chinese stock market fluctuations is studied by comparing the network indicators with the Shanghai Securities Composite Index (SSCI) and the Causeway International Value Index (CIVIX). Through the experimental results, we find that the indicators are indeed ahead of the Chinese stock market. This study is the first attempt to model stock market sentiment by using a complex network, and it proves that investor behavior has a great effect on the stock market.

scholarly journals Analysis on the Impact of Short-Term International Capital Flows on Chinese Stock Market on the Basis of VAR Model

2018 ◽  
Vol 10 (8) ◽  
pp. 77
Author(s):  
Ning Wu
Keyword(s):  
Stock Market ◽  
Capital Flows ◽  
Composite Index ◽  
International Capital Flows ◽  
Capital Inflows ◽  
Var Model ◽  
Chinese Stock Market ◽  
International Capital ◽  
Short Term ◽  
The Impact

With the continuous development of global economic integration and financial markets, international capital flows more and more frequently, the frequent flow of international capital will inevitably affect the yield of Chinese stock market. This article uses short-term international capital inflows SS and Shanghai composite index R as research objects. Based on monthly data from January 2002 to October 2017, VAR model was constructed using Eviews8.0 to study the impact of short-term international capital flows on Chinese stock market. Empirical studies have found that short-term international capital flow is the granger cause of changes in the Shanghai composite index yield, while the yield of Chinese stock market will not affect short-term international capital flows. At the end of this paper, relevant suggestions are put forward according to the conclusions.

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.

Does investor personality moderate the relationship between information sources and trading behavior? Evidence from Chinese stock market

2017 ◽  
Vol 43 (5) ◽  
pp. 545-566 ◽  
Author(s):  
Muhammad Zubair Tauni ◽  
Zia-ur-Rehman Rao ◽  
Hong-Xing Fang ◽  
Minghao Gao
Keyword(s):  
Stock Market ◽  
Personality Traits ◽  
Chinese Stock Market ◽  
Sources Of Information ◽  
Trading Behavior ◽  
Content Type ◽  
Trading Frequency ◽  
Word Of Mouth Communication ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this paper is to investigate the impact of the key sources of information, namely, financial advice, word-of-mouth communication and specialized press, on trading behavior of Chinese stock investors. The study also analyzed if the association between the key sources of information and trading behavior is influenced by investor personality. Design/methodology/approach The authors adopted the Big Five personality framework and examined the survey results of individual stock investors (n=541) in China. Personality traits of investors were measured by the NEO-Five Factor Inventory (Costa and McCrae, 1989). The authors performed probit regression analysis to evaluate the moderating influence of investor personality traits on the association between sources of information and stock trading behavior. Findings The results of the study confirm the previous findings that the key sources of information used by investors as a foundation of their financial choices have a significant influence on their trading behavior. The study also provides empirical evidence that investor personality traits moderate the relationship between the key sources of information and trading behavior. Financial advisors tend to increase the frequency of trading in investors with openness, extraversion, neuroticism and agreeableness personality traits, and tend to decrease the intensity of trading in investors with conscientiousness trait. On the other hand, financial information acquired from word-of-mouth communication is more likely to enhance trading frequency in extraverted and agreeable investors, and is more likely to reduce trading frequency in investors with openness, conscientiousness and neuroticism traits. Finally, the use of specialized press leads to more adjustment in portfolios of the investors with openness and conscientiousness traits than those with other personality traits. An alternative mediated model was not supported. Originality/value This research contributes to information search literature and behavioral finance literature and provides empirical evidence that the psychological characteristics of investors are significant predictors of the variations in information-trading link. The study offers new theoretical insights of investors’ behavior due to the characteristics of Chinese stock market which are unique from other stock markets in the world. To the authors’ best knowledge, no previous study has been conducted so far in Chinese stock market to explore variations with regards to the impact of the key sources of information on trading behavior by the Big Five investor personality and this paper seeks to fill this gap.

An application of the information-adjusted noise model to the Shenzhen stock market

2016 ◽  
Vol 12 (1) ◽  
pp. 71-91 ◽  
Author(s):  
Xiaoming Xu ◽  
Vikash Ramiah ◽  
Imad Moosa ◽  
Sinclair Davidson
Keyword(s):  
Stock Market ◽  
Noise Model ◽  
Chinese Stock Market ◽  
Content Type ◽  
Asset Pricing Model ◽  
Noise Trader ◽  
Pricing Errors ◽  
Trading Activities ◽  
The Relationship ◽  
Information Pricing

Purpose – The purpose of this paper is to: first, test if information-adjusted noise model (IANM) can be applied in China; second, quantify noise trader risk, overreaction, underreaction and information pricing errors in that market; and third, explain the relationship between noise trader risk and return. Design/methodology/approach – The authors use a behavioural asset pricing model (BAPM), CAPM, the information-adjusted noise model and model proposed by Ramiah and Davidson (2010). Findings – The findings show that noise traders are active 99.7 per cent of the time on the Shenzhen A-share market. Furthermore, our results suggest that the Shenzhen market overreacts 41 per cent of the time, underreacts 18 per cent of the time and information pricing errors occur 40 per cent of the time. Originality/value – Various methods have been applied to the Chinese stock market in an effort to measure noise trading activities and all of them failed to account for information arrival. Our study uses a superior and alternative model to detect noise trader risk, overreaction and underreaction in China.

Empirical Study on the Non-Periodic Cycles of Fractal Time Series: Examples of Indices of the Chinese Stock Market

2015 ◽  
Vol 734 ◽  
pp. 637-641
Author(s):  
Yang Li ◽  
Wei Yu Zhang ◽  
Yong Wei ◽  
Jin Hui Sun
Keyword(s):  
Time Series ◽  
Random Walk ◽  
Stock Market ◽  
Composite Index ◽  
Chinese Stock Market ◽  
Biased Random Walk ◽  
Fractal Time ◽  
Study Support ◽  
Two Indices ◽  
Periodic Cycles

By R/S analysis, non-periodic cycles of the SSE Composite Index and SZSE Composite Index are studied in this paper. With a different determinant method from the previous works about fractal behaviors of the Chinese stock market, the empirical results obtained in this study support the non-periodic cycle results but with different values. With more data available, the analysis shows that the two indices follow a biased random walk with two non-periodic cycles, one about 4.5 years and another about 9 years, which may be tied to the economic and politic cycles.

Initial value sensitivity of the Chinese stock market and its relationship with the investment psychology

2015 ◽  
Vol 26 (11) ◽  
pp. 1550128
Author(s):  
Shangjun Ying ◽  
Xiaojun Li ◽  
Xiuqin Zhong
Keyword(s):  
Stock Market ◽  
Cellular Automaton ◽  
Initial Conditions ◽  
Chinese Stock Market ◽  
Initial Value ◽  
The Government ◽  
Evolving Model ◽  
The Relationship ◽  
Evolving Models ◽  
A Share

This paper discusses the initial value sensitivity (IVS) of Chinese stock market, including the single stock market and the Chinese A-share stock market, with respect to real markets and evolving models. The aim is to explore the relationship between IVS of the Chinese A-share stock market and the investment psychology based on the evolving model of genetic cellular automaton (GCA). We find: (1) The Chinese stock market is sensitively dependent on the initial conditions. (2) The GCA model provides a considerable reliability in complexity simulation (e.g. the IVS). (3) The IVS of stock market is positively correlated with the imitation probability when the intensity of the imitation psychology reaches a certain threshold. The paper suggests that the government should seek to keep the imitation psychology under a certain level, otherwise it may induce severe fluctuation to the market.

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 Mutual fund herding behavior and investment strategies in Chinese stock market

2018 ◽  
Vol 15 (2) ◽  
pp. 87-95 ◽  
Author(s):  
John Wei-Shan Hu ◽  
Yen-Hsien Lee ◽  
Ying-Chuang Chen
Keyword(s):  
Stock Market ◽  
Mutual Fund ◽  
Investment Strategies ◽  
Chinese Stock Market ◽  
Contrarian Strategy ◽  
Contrarian Strategies ◽  
Low Degree ◽  
High Degree ◽  
The Impact ◽  
The Relationship

This investigation studies the impact of mutual fund herding on the returns achieved by contrarian strategy from 1990 to 2015 in the Chinese stock market. The relationship between the profit gained by the contrarian strategy and the macroeconomic environment is also examined. First, the returns of the contrarian strategy in China’s stock market are found to be significant. Second, most loser stocks with a high degree of mutual fund herding outperform loser stocks with a low degree of mutual fund herding, revealing that the profitability of an investment portfolio depends on the degree of mutual fund herding. Third, investors should buy loser stocks with a high degree of herding and sell winner stocks with a low degree of herding during a two-year formation period, over which zero-cost contrarian strategies yield the significantly highest return. Finally, the payoff of contrarian strategies is positively related to the herding effect and negatively related to macroeconomic variables.

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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