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This paper aims to investigate Chinese bull and bear markets. The Chinese stock market has experienced a long period of bear cycle from early 2000 until 2006, and then it fluctuated greatly until 2010. However, the cyclical behaviour of stock markets during this period is less well established. This paper aims to answer the question why the Chinese stock market experienced a long duration of bear market and what factors would have impacted this cyclical behaviour.

By comparing the intervals of bull and bear markets between stocks and indices based on a Markov switching model, this paper examines whether different industries or A- and B-share markets could lead to different stock market cyclical behaviour and whether firm size can determine the relationship between the firm stock cycles on the market cycles.

This paper finds a high degree of overlapping of bear cycles between stocks and indices and a high level of overlapping between the bear market and a fraction of stock with increasing stock prices. This leads to the conclusion that the stock performance and trading behaviour are widely diversified. Furthermore, the paper finds that the same industry may have different overlapping intervals of bull or bear cycles in the Shanghai and Shenzhen stock markets. Firms with different sizes could have different overlapping intervals with bull or bear cycles.

This paper fills the literature gap by establishing the cyclical behaviour of stock markets.

The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period.

This study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model.

The wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets.

This study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.

The focus of the current research is to examine whether mixed-frequency investor sentiment affects stock volatility in the China A-shares stock market.

Mixed-frequency sampling models are employed to find the relationship between stock market volatility and mixed-frequency investor sentiment. Principal analysis and MIDAS-GARCH model are used to calibrate the impact of investor sentiment on the large-horizon components of volatility of Shanghai composite stocks.

The results show that the volatility in Chinese stock market is positively influenced by B–W investor sentiment index, when the sentiment index encompasses weighted mixed frequencies with different horizons. In particular, the impact of mixed-frequency investor sentiment is most significantly on the large-horizon components of volatility. Moreover, it is demonstrated that mixed-frequency sampling model has better explanatory powers than exogenous regression models when accounting for the relationship between investor sentiment and stock volatility.

Given the various unique features of Chinese stock market and its importance as the major representative of world emerging markets, the findings of the current paper are of particularly scholarly and practical significance by shedding lights to the applicableness GARCH-MIDAS in the focused frontiers.

A more accurate and insightful understanding of volatility has always been one of the core scholarly pursuits since the influential structural time series modeling of Engle (1982) and the seminal work of Engle and Rangel (2008) attempting to accommodate macroeconomic factors into volatility models. However, the studies in this regard are so far relatively scarce with mixed conclusions. The current study fills such gaps with improved MIDAS-GARCH approach and new evidence from Shanghai A-share market.

The purpose of this paper is to examine the stock return impact of “lucky” numbered days in markets dominated by Chinese participants. The existence of such patterns might present arbitrage opportunities for investors who do not share a belief in the Chinese system of “lucky” numbers.

In univariate and multivariate analyses, the author examines the statistical significance of return differences between “lucky” numbered days and other days. The author examines samples which only consider single digit days and months, and the author also considers samples based on the last digit of the day or month. Based on the findings in these tests, the author designs and tests a trading strategy on the Shenzhen Exchange that produces significant risk-adjusted returns in excess of the buy-and-hold return on the Shenzhen Composite Index.

The author shows that “lucky” numbered dates impact stock returns in Chinese markets and demonstrate a “lucky” number date trading strategy for the Shenzhen market that produces risk-adjusted returns in excess of the market return.

Prior research on home address numbers and stock trading codes shows that, in markets dominated by Chinese participants, assets with identifiers containing numbers defined by Feng Shui as “lucky” sell at a premium and assets with identifiers containing “unlucky” numbers sell at a discount. In such markets, prices are more likely to end in a “lucky” number than an “unlucky” number. Chinese firms also tend to price their shares at IPO using “lucky” numbers and avoiding “unlucky” numbers. The author extends this literature to examine whether dates containing “lucky” and “unlucky” numbers experience stock returns significantly different than other days on Chinese stock exchanges.

The COVID-19 pandemic has recently had an impact on the stock market all over the globe. A thorough review of the literature that included the most cited articles and articles from well-known databases revealed that earlier research in the field had not specifically addressed how the BRIC stock markets responded to the COVID-19 pandemic. The data regarding COVID-19 were collected from the World Health Organization (WHO) website, and the stock market data were collected from Yahoo Finance and the respective country’s stock exchange. A random forest regression algorithm takes the closing price of respective stock indices as target variables and COVID-19 variables as input variables. Using this algorithm, a model is fit to the data and is visualised using line plots. This study’s findings highlight a relationship between the COVID-19 variables and stock market indices. In addition, the stock market of BRIC countries showed a high correlation, especially with the Shanghai Composite Stock Index with a correlation value of 0.7 and above. Brazil took the worst hit in the studied duration by declining approximately 45.99%, followed by India by 37.76%. Finally, the data set’s model fit, which employed the random forest machine learning method, produced R² values of 0.972, 0.005, 0.997, and 0.983 and mean percentage errors of 1.4, 0.8, 0.9, and 0.8 for Brazil, Russia, India, and China (BRIC), respectively. Even now, two years after the coronavirus pandemic started, the Brazilian stock index has not yet returned to its pre-pandemic level.

The purpose of this paper is to examine the dynamic relationship between crude oil price volatility and stock markets in the emerging economies like BRIC (Brazil, Russia, India and China) countries in the context of sharp continuous fall in the crude oil price in recent times.

The stock price volatility is partly explained by volatility in crude oil price. The author adopt an Asymmetric Power ARCH (APARCH) model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

For Bovespa, MICEX, BSE Sensex and crude oil there is an asymmetric response of volatilities to positive and negative shocks and negative correlation exists between returns and volatility indicating that negative information will create greater volatility. However, for Shanghai Composite positive information has greater effect on stock price volatility in comparison to negative information. The study results also suggest the presence long memory behavior and persistent volatility clustering phenomenon amongst crude oil price and stock markets of the BRIC countries.

The present study makes a number of contributions to the existing literature in the following ways. First, the author have considered crude oil prices up to January 31, 2016, so that the study can reflect the impact of declining trend of crude oil prices on the stock indices which is also regarded as “new oil price shock” to measure the volatility between crude oil price and stock market indices of BRIC countries. Second, the volatility is captured by APARCH model which takes into account long memory behavior, speed of market information, asymmetries and leverage effects.

This study aims to investigate the impact of the COVID-19 pandemic on both of stock prices and investor's sentiment in China during the onset of the COVID-19 crisis.

In this study, the ADCC-GARCH model was used to analyze the asymmetric volatility and the time-varying conditional correlation among the Chinese stock market, the investors' sentiment and its variation. The authors relied on Diebold and Yilmaz (2012, 2014) methodology to construct network-associated measures. Then, the wavelet coherence model was applied to explore the co-movements between these variables. To check the robustness of the study results, the authors referred to the RavenPack COVID sentiments and the Chinese VIX, as other measures of the investor's sentiment using daily data from December 2019 to December 2021.

Using the ADCC-GARCH model, a strong co-movement was found between the investor's sentiment and the Shanghai index returns during the COVID-19 pandemic. The study results provide a significant peak of connectivity between the investor's sentiment and the Chinese stock market return during the 2015–2016 and the end of 2019–2020 turmoil periods. These periods coincide, respectively, with the 2015 Chinese economy recession and the COVID-19 pandemic outbreak. Furthermore, the wavelet coherence analysis confirms the ADCC results, which revealed that the used proxies of the investor's sentiment can detect the Chinese investors' behavior especially during the health crisis.

This study provides two main types of implications: on the one hand, for investors since it helps them to understand the economic outlook and accordingly design their portfolio strategy and allocate decisions to optimize their portfolios. On the other hand, for portfolios managers, who should pay attention to the volatility spillovers between investor sentiment and the Chinese stock market to predict the financial market dynamics during crises periods and hedge their portfolios.

This study attempted to examine the time-varying interactions between the investor's sentiment proxies and the stock market dynamics. Findings showed that the investor's sentiment is considered a prominent channel of shock spillovers during the COVID-19 crisis, which typically confirms the behavioral contagion theory.