Contagion risk prediction with Chart Graph Convolutional Network: Evidence from Chinese stock market

This study aims to investigate bidirectional risk spillovers between the Chinese and other Asian stock markets. To achieve this, we construct a dynamic Copula-EVT-CoVaR model based on 11 Asian stock indexes from 1 January 2007 to 31 December 2021. The findings show that, firstly, synchronicity exists between the Chinese stock market and other Asian stock markets, creating conditions for risk contagion. Secondly, the Chinese stock market exhibits a strong risk spillover to other Asian stock markets with time-varying and heterogeneous characteristics. Additionally, the risk spillover displays an asymmetry, indicating that the intensity of risk spillover from other Asian stock markets to the Chinese is weaker than that from the Chinese to other Asian stock markets. Finally, the Chinese stock market generated significant extreme risk spillovers to other Asian stock markets during the 2007–2009 global financial crisis, the European debt crisis, the 2015–2016 Chinese stock market crash, and the China–US trade war. However, during the COVID-19 pandemic, the risk spillover intensity of the Chinese stock market was weaker, and it acted as the recipient of risk from other Asian stock markets. The originality of this study is reflected in proposing a novel dynamic copula-EVT-CoVaR model and incorporating multiple crises into an analytical framework to examine bidirectional risk spillover effects. These findings can help Asian countries (regions) adopt effective supervision to deal with cross-border risk spillovers and assist Asian stock market investors in optimizing portfolio strategies.

To compare and analyze the difference between the United States and Chinese stock market, the relative variance, correlation, beta, and volatility of different stock markets are required. These parameters could help people to make better decisions and risk predictions while investing in China or the United States. The analysis also shows cases and the unique characteristics of both the Chinese and United States stock market, leading to the conclusion that the United States stock market is more mature and stable than the Chinese stock market. Through the comparison between the two stock markets, the Chinese and American economies could be better understood.

Whether the long-memory property is inherent in the movement of the stock market returns and volatility (risk) time series is known as a very important issue practically or theoretically regard to the efficiency of the stock market. The efficient market hypothesis describes that the information obtained from past statistics can not be used to predict the future stock price. This is because when generating the information that may affect the price of the stock reflects this value of the information on the price quickly and enough. Since the future information is unknown, the future stock price will not be predictable if the stock market is efficient. However, if the long-memory property exists in the stock market returns and volatility time series, it could predict a certain portion of the future returns and risks by using past data. This predictability means the assumption of classical investment theory, that the stock market is efficient, may not be proper. Thus, the existence of long-memory property has been addressed as an important research topic by the financial investment researchers and stock market investors. By using the stock prices of 50 stocks representing the Chinese stock market and their weighted average statistical index - SSE 50 Index, this study analyzes whether the long-memory property is inherent in Chinese stock market price movement as well as explains whether the existence of long-memory property is spurious result of the contemporaneous aggregation. The Chinese stock market is extremely proper market to perform the research related to the long-memory property because it is large and highly dynamic market. Using the returns and volatility of daily closing price (i.e. the absolute value of returns and its squared value) from January 2, 2004 to December 10, 2014 to conduct the Lo’s modified (R/S) analysis and the Geweke-Porter-Hudak (GPH) test. The main results of the empirical analysis from this study are as follows. First, although SSE 50 Index return series has long-memory property, there are not many evidences for its 50 constituent company stock prices. This means that predicting the return series for SSE 50 Index is relatively easier than individual stock prices. Second, in the case of volatility, both of the SSE 50 Index and its 50 individual stock prices have the presence of a long-memory property. Third, most of the 50 individual stock prices in Chinese market have the long-memory property. These are the unique properties inherent in the stock market time series instead of causing by the spurious consequence of a contemporaneous aggregation bias. Fourth, volatility has the stronger presence of a long-memory property than returns. This means that predicting the risk is relatively easier than returns due to volatility clustering. Based on the overall statistical test results, volatility has the stronger presence of a long-memory property than returns. The long-memory property exists in the Chinese stock market and this is the unique property inherent in the stock market time series instead of causing by the spurious consequence of a contemporaneous aggregation bias. These analytical findings indicate that the Chinese stock market is not fully efficient due to the existence of the long-memory property. The reasons that Chinese stock market is not efficient enough are that many Chinese investors have the speculative purposes and market information is not delivered transparently and quickly. Because of these characteristics, global investors will have room to reduce the risk and increase profits by leveraging long-memory properties in the Chinese stock market.

Multi-scale risk contagion among international oil market, Chinese commodity market and Chinese stock market: A MODWT-Vine quantile regression approach

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.

As global financial markets become highly dependent on each other, risk contagion among stock markets is a primary feature of progressing globalization, which poses uncertainties for government agencies. The deficiency of previous studies is that it is difficult to accurately grasp the direction of risk diffusion in different time periods, and to depict the intensity of risk contagion constantly. Research on causality and measurement of financial risk contagion based on nonlinear causality tests and dynamic Copula methods will help governments to allocate financial resources reasonably and effectively, thus promoting the sustainable development of the social economy and financial markets. Taking the Chinese stock market as an example, this paper evaluated the risk contagion effect between the Chinese stock market and six other stock markets including developed and emerging markets from January 2006 to December 2018. From the aspect of causality, the nonlinear Granger causality test was applied to the entire time period and the phased time periods involving specific events like the subprime mortgage crisis and the Chinese stock market crash. From the aspect of measurement, the dynamic Markov state transition Copula model was used to describe the asymmetrically dependent structure of markets, from which was derived the time-varying lower tail dependence coefficients. The results have been summarized as follows. Firstly, after the outbreak of the subprime mortgage crisis, the stock markets in developed and emerging markets unilaterally affected the Chinese stock market, indicating that China was the recipient at this stage. Then, after the outbreak of the Chinese stock market crash, the Chinese stock market had a risk contagion effect on both Japanese and Russian stock markets, indicating that China became a source of financial risk contagion within a limited area at this stage. Lastly, in terms of the degree of risk contagion, the lower tail dependence coefficients of the Chinese stock market and other markets were significantly increased after the occurrence of specific risk events, while the risk contagion degree of developed markets was higher than that of emerging markets. Policymakers can recognize and apply the characteristics of risk contagion at different stages to refrain from unreasonable institutional arrangements, thus improving the sustainability of economic development.

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.

By considering two time windows of crises, first one is the time period of Asian financial crisis (1997-1999) and the other one is prevailing global economic crisis (2007-2009), the pattern of underpricing and aftermarket performance are studied. A sample of 626 companies and Market adjusted return model are used. Result indicates that in the recent global economic crisis IPO activity is on shrinking trend and there is 10% increase in average underpricing as compared to last Asian financial crisis. There is a fluctuating trend in aftermarket performance of IPO returns. A minimum return of 62% in 2009 is observed. This study also endeavors to examine the efficiency of Chinese stock market and how the Asian and global financial crisis influences the efficiency of Chinese stock market. In order to determine the efficiency of Chinese stock market we apply efficient market hypothesis of random walk. Here we apply ADF, DF-GLS, PP and KPSS tests on stock market returns in order to check the unit root in data series for both Shenzhen and Shanghai stock exchanges separately. The results of the study shows that Chinese stock market is weak form efficient and past data of stock market movements may not be very useable in order to make excess returns. In both periods of crises Chinese stock market is observed weak form efficient.

In this article, we analyze the characteristics of the implicit cycle of volatility in Chinese stock market by the theory of frequency spectrum. Through searching literature, we know the fact that the study of volatility in Chinese stock market always concentrate their attention on existence of volatility and there is lack of research on the implicit cycle characteristic in the market volatility. In recent years, some scholars also study the volatility in Chinese stock market and hold that there is implicit cycle of volatility in Chinese stock market, but don't provide the statistical test about peak value. The essence of implicit cycle in volatility is the performance of the low efficient market. Therefore, in this article, we establish the periodgram analysis model, and apply the window spectrum estimate of the power spectrum to analyze the volatility of Shanghai's stock price index and Shenzhen's. We also study the existence on implicit cycle of volatility in Chinese stock market in order to determine the improvement degree about Chinese stock market's efficiency. In this article, we study the implicit cycle of volatility in Chinese stock market by the stock index. The volatility of stock market is referring to the volatility that corresponded to the stock index. The author selects the day closing quotation index of Shanghai stock exchange composite index and of Shenzhen stock exchange component index as data sample and the data sector is from January 4, 1999 to December 13, 2005, amount to 1668 trading day. We each establish the two index's day return rate's percentage sequence. The data sequence doesn't have the tendency and seasonal characteristic. We apply the SPECTRA process of the SAS software (spectral analysis process) to determine the sequence's implicit cycle and provide the statistical test about peak value. So we obtain some researches output. We hold that there does not exist the implicit cycle of volatility in Chinese stock market. From this research we know that the Chinese stock market efficiency obtains the enhancement and the volatility structure have a greater change than several year ago. We also believe that the higher volatility in Chinese stock market is may caused by the centralized and fierce new message and by the worse market absorbency in the shock of message. Both lead to the stock price's volatility

This paper tests the volatility spillover effects between Chinese and international stock market from 2000 to 2019 by using asymmetric EGARCH model. Through the empirical analysis on the five indices’ daily data, it is found that the volatility spillover effect of international stock market on Chinese stock market is not significant across the whole sample period. Ever since the opening of the Shanghai-Hong Kong Stock Connect and the Shenzhen-Hong Kong Stock Connect, however, the volatility spillover effect between Chinese and international market has been strengthened compared to the past. Based on the research conclusions, this paper puts forward some suggestions for the further development of Chinese capital market.

In the wake of the burgeoning field of quantitative finance, factor models have risen to considerable prominence within the Chinese financial landscape. This research undertakes a comprehensive analysis employing data spanning the years 2014 to 2022, encompassing a total of 108 monthly observations sourced from the Chinese A-share stock market. The principal objective of this study revolves around the evaluation of the effectiveness of an array of common factors. These factors include the market risk premium, market value, book-to-market ratio, profitability, investment, momentum, and liquidity factors. Moreover, our inquiry extends beyond the conventional boundaries of the Fama-French three-factor and five-factor models. It introduces the pivotal elements of momentum and liquidity factors, effectively formulating an enriched model poised to offer a more robust framework for understanding and explaining returns. The discerned findings shed valuable light on the model configurations most apt for dissecting the excess returns exhibited within the sample stocks. Notably, the amalgamation of the three-factor model with the strategic incorporation of the liquidity factor emerges as the most comprehensive explanatory model for the observed excessive returns in the context of the Chinese A-share stock market. It is important to highlight that the introduction of the momentum factor, while explored, does not impart a significant augmentation in the model's capacity to clarify excess returns, a noteworthy departure from expectations within the Chinese A-share market. These insights not only advance our understanding of the Chinese financial landscape but also underscore the intricate dynamics at play in the realm of quantitative finance.

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.

Does oil product pricing reform increase returns and uncertainty in the Chinese stock market?

I. INTRODUCTIONThe book-to-market effect (otherwise known as the effect) is an empirical regularity that stocks with high book-to-market (BM) ratios (low market prices to the book values of equity) earn higher average (riskadjusted) returns than stocks with low BM ratios. Many previous asset pricing studies suggest that the existence of premium can be explained from either the perspective of risk or the influence of mispricing factors.? Findings from these asset pricing studies extensively rely on datasets from the U.S. stock market which not only has a large pool of global institutional investors but also is considered a relatively efficient market.Previous studies such as Fama and French (1998 and 2012) and Asness, Moskowitz and Pedersen (2013) have also confirmed the existence of premium in international financial markets. However, premium could exist in various markets with different explanations. For example, risk-based explanation has built on the efficient market hypotheses. Those explanation may fit in the U.S. market, but not in other less developed and less efficient markets such as the Chinese market. Drew, Naughton, and Veeraraghavan (2003) and Euna and Huang (2007) have shown that the premium exists in Chinese markets. However, they have not provided its explanation.Unlike previous studies, this study goes further by providing the explanation for the existence of premium in Chinese markets. We notice that the Chinese stock market is a natural candidate for testing whether individual investors drive premium with the following reasons. First, the mainland Chinese stock markets are often perceived as casinos driven by fast money flows in and out of stocks with little regard for their underlying value (Wall Street Journal, August 22 2001). In addition, the segmentation of the markets and the predominance of individual investors in these markets make the Chinese stock market a natural candidate for testing whether individual investors drive premium.Our results confirm that, like Drew et al. (2003) and Eun and Huang (2007), the premium does exist in the Chinese markets for the period from 1994 to 2010. Moreover, there is a significantly negative relationship between institutional ownership of stocks and premium. This is apparently consistent with findings of Phalippou (2007, 2008) that premium is related to trading activities of individual investors, not institutional investors in the U.S. market.Our findings with the Chinese firms contribute to empirical asset pricing literature by providing international supporting evidence that the premium could be driven by individual investors, whereas stocks that are mostly held by institutional investors are value-premium free.The rest of the paper is organized as follows. Section 2 reviews the literature regarding the premium, the characteristics of the Chinese stock markets and develops several testable hypotheses. Section 3 then discusses the datasets and explains the empirical methodology. The empirical results are analyzed in detail in Section 4. Finally, this paper concludes by discussing the implications of our results for policy regulators, the role of institutional investors, and the overall market efficiency in China.II. THE VALUE PREMIUM AND CHINESE STOCK MARKET1. The Value Premium and Its ExplanationThe economic interpretation of the premium is a much debated issue, with current explanations falling into two broad categories. One explanation suggests that the premium is compensation for risk that is not captured by the capital asset pricing model (Fama and French, 1995, 1996, 1998; Lindaas and Simlai, 2014). In particular, Fama and French suggest that the premium is apparently related to the degree of relative in the economy. When the economy weakens, investors demand a higher risk premium on firms with distress characteristics. …

We propose a new linear model to explain the price move by Level-2 high-frequency data in Chinese mainland stock market. In Chinese stock market, the cancellation ratio is very low, and imbalanced order flow prevails most of the time in the trading periods. From time dimension viewpoint, we find the difference of efficiency of limit orders executed, respectively, in bid/ask limit order book, order execution imbalance (OEI), could improve the classic model of Cont et al. (2014) based on market microstructure of Chinese mainland stock market. In particular, when market’s liquidity is booming, our model’s explanatory power and R-squared increased sharply. And the correlations of OEI are very high that may be exploited to predict the price move in the next time window for doing high-frequency trading.