Common institutional ownership can enhance knowledge spillovers by increasing portfolio firms’ awareness about each other’s innovation. By investigating listed electronic hardware firms in China for 2000-2016, we find that when common ownership by mutual funds is higher between a firm pair, it is more likely that these two firms cite each other’s patents. To confirm causality, we show that even the exogenous increase in firms’ common ownership following their inclusion into the stock index still positively influences the citing likelihood. We also find that such citations are taken place in a timely manner. Additionally, this positive effect is robust when the effects of overlapping board members and common ownership by other types of institutional investors are controlled for. This effect is more pronounced among nonneighboring firms, when non-neighboring firms are close to their common owners, when common owners hold shares longer, and when firms’ executives have lower incentive to communicate (i.e., SOEs). Last, we find that common ownership by mutual funds also enhances knowledge spillovers through third-party patents. This paper deepens the understanding of knowledge spillovers among firms in developing countries.

This paper is the first study to systematically discuss the predictive power of crosssectional asset pricing anomalies on aggregate market excess return time series in the Chinese A-share market. The paper summarizes the anomalies and uses linear methods with different shrinkage techniques to extract predictive information from highdimensional long-short anomaly portfolio returns datasets. We find that long-short anomaly portfolio returns show highly significant out-of-sample predictive power of aggregate market excess returns, both statistically and economically. Unlike similar studies on U.S. stocks, the predictive power stems from stronger limits of arbitrage in the short-leg when using bid-ask spread as a proxy but from stronger limits of arbitrage in the long-leg when idiosyncratic volatility or market capitalization is used as proxies.

The COVID-19 pandemic has inflicted substantial impacts on global financial markets and the economy. This study explores the impact of two pandemic outbreaks in China on its stock market industries. It employs the Conditional Autoregressive Value at Risk (CAViaR) model to compute tail risks across 16 selected industry sectors. Additionally, risk correlation networks are constructed to illustrate the risk correlations among industry sectors during different phases of the two outbreaks. Furthermore, risk contagion networks are built based on the Granger causality test to examine the similarities and differences in the contagion mechanisms between the two outbreaks. The findings of this study show that (i) the two outbreaks of COVID-19 have resulted in tail risks for most industries in the Chinese stock market. (ii) The risk correlation network became more compact because of both outbreaks. The impact of the second outbreak on the network was less severe than that of the first outbreak. (iii) During the first outbreak of COVID-19, the financial industry was the primary source of risk output; during the second outbreak, the concentrated outbreak in Shanghai led the industries closely related to the city's economy and trade to become the most significant risk industries. These findings have practical implications for researchers and decision-makers in terms of risk contagion among stock market industries under major public emergencies.

The influence of religious beliefs on investment is interesting and important in literature. We use a large dataset with detailed information on the worship places of the five largest religious groups in China to study the relationship between local religious beliefs and individual financial decisions. We find that Buddhists, Taoists, Islamists, and Catholics are less likely to buy financial products on the financial market; Protestants tend to take risks compared to other believers. Chinese Protestants, unlike American Christians, seek more risk, while Catholics are risk-averse.

This paper investigates the free cash flow productivity of SOEs compared with non-SOEs and examines its possible determinants. We find that SOEs have slightly weak free cash flow productivity but significantly stronger than non-SOEs. Similar performance exists among commercial class I and II SOEs and public-benefit SOEs. Further analyses suggest that firm size, age, sales growth, ownership concentration, government subsidies, and industry monopoly factors cannot explain this phenomenon. The common driver for all types of SOEs to generate stronger free cash flows than nonSOEs is their stronger expense control capability.

This paper researches into development of China’s shadow banking during 2020-2022, a special period marked by COVID-19 and strengthened global regulation on Non-Bank Financial Intermediation (NBFI). Research focus includes balance sheet evolvement, growth dynamics, and relation with macro-finance. Its business model surprisingly resembles western peers. They both fund underserved sectors and have similar exposure to balance sheet mismatch. Massive holding of bond investment (36.6% of total asset) is funded by uninsured interbank fund and wealth management product, which makes it more closely related with banks’ balance sheet and risk contagion from NBFI to traditional commercial banks more easily. This paper then re-summarizes growth dynamics of China’s shadow banking in a “Pull-Push” framework, and proposes concept of reintermediation in respective to disintermediation. Consecutive regulation on NBFI and real estate sector kept dragging on growth of shadow banking, and rendered it in liquidity surplus, which is invested into interbank market. This paper also provides empirical evidence on relation of China’s shadow banking with macro-finance, and notes several empirical breakdowns of pre- COVID relations among economic and financial indicators. Most important breakdown is the non-functionality of monetary policy transmission channel. Besides, it continued to twist de facto financial regulatory indicators, however with fading impact.

Research has argued that state-owned enterprises (SOEs) should bear more social responsibility than other listed firms, because their own goals include maintaining social stability and promoting social welfare. In contrast with the privatization of SOEs observed in other countries, in China, some listed firms’ major shareholders have become SOEs in recent years. This transition offers a good opportunity to investigate the impact of ownership change on firms’ corporate social responsibility (CSR). Using the propensity score matching difference-in-differences method, we document that the CSR performance of these firms does not improve when their ownership structure changes, and it can even worsen. Our results remain robust to a series of tests. Further investigating the underlying economic mechanism, we uncover those political connections, bank financing, and government subsidies play critical roles in determining the negative effect of ownership structure change on public firms, which is consistent with the soft budget constraint framework. In an additional analysis, we find that CSR performance is poor for manufacturing industry firms after ownership structure change. After calculating the frequency of keywords appearing in the annual reports of such firms, we find them to be satisfied with their new SOE background after ownership structure change. Our paper provides a possible explanation for the phenomenon of SOEs becoming major shareholder of listed firms.

We track the fundamental informed traders' (FITs) behavior and show the fundamental momentum effect in the Chinese stock market. We train the deep learning model with a set of fundamental characteristics to extract fundamental implied component from realized returns. The fundamental part characterizes the price movement driven by FITs. Fundamental momentum differentiates from the fundamental trend and is not quality minus junk (QMJ) factor. Underreaction bias helps explain the strategy, as it generates stronger profit during periods of low investor sentiment and aggregate idiosyncratic volatility. Fundamental momentum is not sensitive to changing beta and robust in subsamples and machine learning models.

We examine the effects of China's 2009–2010 stimulus package for innovation differentials between state-owned firms (SOEs) and privately-owned firms (POEs). Using a unique dataset of Chinese manufacturing firms, we find that in the pre-stimulus period SOEs patent at a lower rate than POEs in the least inventive patent category, and at a comparable rate in the more inventive categories. Post-stimulus, SOEs patent at an even lower rate relative to POEs in the least inventive category, but significant, positive SOE-POE patent rate differentials emerge in more inventive patent categories. The stimulus disproportionately benefited SOEs with higher investment subsidies and lower finance costs—institutional support which we find mediates roughly 45 percent of all positive effects of state-ownership for innovation. Institutional support produces larger SOE-POE innovation differentials among firms in strategic sectors and located in high-marketization provinces, and for centrally controlled SOEs.

This paper employs a new empirical approach to estimate the impact of subsidies on growth and productivity. Our key innovation is to use local political leader geographic rotation as a source of exogenous variation. By using Chinese Industrial Census data from 1999 to 2013, we find that more subsidies have a positive effect on growth but not on productivity. Further firm-level results suggest that the size expansion of firms to win subsidies might be the mechanism, a new spillover channel, in explaining our main finding.