This paper examines the daily return series of four main indices, including Shanghai Stock Exchange Composite Index (SSE), Shenzhen Stock Exchange Component Index (SZSE), Shanghai Shenzhen 300 Index (SHSE-SZSE300), and CSI Smallcap 500 index (CSI500) in Chinese stock market from 2000 to 2018 by multifractal detrended fluctuation analysis (MF-DFA). The series of the daily return of the indices exhibit significant multifractal properties on the whole time scale and SZSE has the highest multifractal properties among the four indices, indicating the lowest market efficiency. The multifractal properties of four indices are due to long-range correlation and fat-tail characteristics of the non-Gaussian probability density function, and these two factors have different effects on the multifractality of four indices. This paper aims to compare the multifractility degrees of the four indices in three sub-samples divided by the 2015 stock market crash and to discuss its effects on efficiency of the Shanghai and Shenzhen stock market in each sub-sample. Meanwhile, we study the effect of the 2015 stock market crash on market efficiency from the statistical and fractal perspectives, which has theoretical and practical significance in the application of Effective Market Hypothesis (EMH) in China’s stock market, and it thereby affects the healthy and sustainability of the market. The results also provide important implications for further study on the dynamic mechanism and efficiency in stock market and they are relevant to portfolio managers and policy makers in a number of ways to maintain the sustainable development of China’s capital market and economy.

This paper constructs a theoretical model to analyze the effect of macroprudential policies (MPPs) on bank risk-taking. We collect a data set of 231 commercial banks in China to empirically test whether macroprudential tools, including countercyclical capital buffers, reserve requirements, and caps on loan-to-value, can affect bank risk-taking behaviors by using the dynamic unbalanced panel system generalized method of moment (SYS-GMM). The results provide further evidence on the important role of MPPs in maintaining financial stability, which helps mitigate financial system vulnerabilities. Bank risk-taking will be decreased with the strengthening of macroprudential supervision, which greatly benefits the resilience and the sustainability of bank sector. Moreover, the credit cycle has a magnifying role on MPPs’ effect on bank risk-taking. Reducing risks in bank loans requires a further slowing of credit growth, which is necessary to ensure sustainable growth in a bank system, or more ambitiously, to smooth financial booms and busts. The results survive robustness checks under alternative estimation methods and alternative proxies of bank risk-taking and MPPs.

Based on carbon spot prices selected from seven carbon pilots, we assess the financial performances related to carbon volatility in China on the overall perspective. According to the results, the Chinese carbon market fluctuated severely at the beginning of carbon trading, but has stabilised in general, despite several dramatic changes related to ‘yearly compliance events’. Long-term memory exists in the volatility series. Moreover, asymmetry exists in the Chinese carbon market, and volatility reacts more severely to good news than to bad news. Finally, we discuss our empirical results, and make certain suggestions regarding firms’ awareness, international cooperation and individual investors not only for policy makers in China but also for other developing countries who are contemplating either commencing carbon trading or improving the current market.

This study examines the causal nexus between defense expenditure and economic growth in China using the bootstrap Granger full-sample causality test and rolling-window estimation. The full-sample result indicates a positive bidirectional causality between economic growth and defense expenditure, suggesting that more defense spending increases economic growth, and vice versa. By adopting a time-varying rolling-window approach to revisit the dynamic causal relationships, this article identifies that the causality changes over time. We find significant positive short-run causality running from economic growth to defense expenditure in most of the time investigated, thus implying that economic growth stimulates defense expenditure. However, large-scale disarmaments break such positive linkage. Conversely, both positive and negative effects of defense expenditure on economic growth are demonstrated, showing that more defense spending has ambiguous effect on economy. Consequently, economic growth mainly drives defense expenditure rather than the other way around. The impact of defense expenditure in China on national economy is affected by multiplier effect and crowding-out effect as well as institutional factors.

This study provides new insights into the relationships between social media sentiments and the stock market in China. Based on machine learning, we classify microblogs posted on Sina Weibo, a Twitter’s variant in China into five detailed sentiments of anger, disgust, fear, joy, and sadness. Using wavelet analysis, we find close positive linkages between sentiments and the stock return, which have both frequency and time-varying features. Five detailed sentiments are positively related to the stock return for certain periods, particularly since October 2014 at medium to high frequencies of less than ten trading days, when the stock return is undergoing significant fluctuations. Sadness appears to have a closer relationship with the stock return than the other four sentiments. As to the lead-lag relationships, the stock return causes Weibo sentiments rather than reverse for most of the periods with significant linkages. Compared with polarity sentiments (negative vs. positive), detailed sentiments provide more information regarding relationships between Weibo sentiments and the stock market. The stock market exerts positive effects on bullishness and agreement of microblogs. Meanwhile, agreement leads the stock return in-phase at the frequency of approximately 40 trading days, indicating that less disagreement improves certainty about the stock market.