Why is price momentum absent in China? Since momentum is commonly considered arising from investors’ under-reaction to fundamental news, we decompose monthly stock returns into news- and non-news-driven components and document a news day return continuation along with an offsetting non-news day reversal in China. The non-news day reversal is particularly strong for stocks with high retail ownership, relatively less recent positive news articles, and limits to arbitrage. Evidence on order imbalance suggests that stock returns overshoot on news days due to retail investors' excessive attention-driven buying demands, and mispricing gets corrected by institutional investors on subsequent non-news days. To avoid this tug-of-war in stock price, we use a signal that directly captures the recent news performance and re-document a momentum-like underreaction to fundamental news in China.

Using the unique regulatory setting from the Hong Kong stock market with both shortable and no-short stocks, we document that no-short stocks on average earn significantly higher average returns than shortable stocks. Furthermore, stocks that comove more with the portfolio of no-short stocks than with the portfolio of shortable stocks on average earn higher subsequent abnormal returns. Additions to and deletions from the shorting list only partially contribute to the no-short return premium. To interpret our findings, we provide a theoretical model showing that rational investors’ discounting for the mispricing risk of no-short stocks can lead to the no-short return premium.

This study investigates the effectiveness of the volatility-timing strategy in the Chinese equity market. We find that the volatility-managed portfolio (VMP) consistently outperforms its original counterpart, both in individual factor analysis and mean-variance efficient multifactor assessment, and the results are robust in outof-sample setup. Notably, the outperformance is mostly driven by stocks with high arbitrage risk, short-selling constraints, relatively smaller size, and lottery preferences. Further, the multifactor portfolio constructed from the volatility-managed strategy outperforms other portfolios especially in turmoil periods such as high sentiment and low macroeconomic confidence periods. Our findings suggest that in the Chinese equity market with typical trading frictions, volatility timing strategies consistently gain profitable performance.

A lack of sufficient information about potential borrowers is a major obstacle to access to financing from the traditional financial sector. In response to the need for better information to prevent fraud, to increase access to finance and to support balanced sustainable development, countries around the world have moved over the past several decades to develop credit information reporting requirements and systems to improve the coverage and quality of credit information. Until recently, such requirements mainly covered only banks. However, with the process of digital transformation in China and around the world, a range of new credit providers have emerged, in the context of financial technology (FinTech, TechFin and BigTech). Application of advanced data and analytics technologies provides major opportunities for both market participants – both traditional and otherwise – as well as for credit information agencies: by utilizing advanced technologies, participants and credit reporting agencies can collect massive amounts of information from various online and other activities (‘Big Data’), which contributes to the analysis of borrowing behavior and improves the accuracy of creditworthiness assessments, thereby enhancing availability of finance and supporting growth and development while also moderating prudential, behavioral and conduct related concerns at the heart of financial regulation. Reflecting international experience, China has over the past three decades developed a regulatory regime for credit information reporting and business. However, even in the context of traditional banking and credit, it has not come without problems. With the rapid growth and development of FinTech, TechFin and BigTech lenders, however, have come both real opportunities to leverage credit information and data but also real challenges around its regulation. For example, due to fragmented sources of borrower information and the involvement of many players of different types, there are difficulties in clarifying the business scope of credit reporting and also serious problems in relation to customer protection. Moreover, inadequate incentives for credit information and data sharing pose a challenge for regulators to promote competition and innovation in the credit market. Drawing upon the experiences of other jurisdictions, including the United States, United Kingdom, European Union, Singapore and Hong Kong, this paper argues that China should establish a sophisticated licensing regime and setout differentiated requirements for credit reporting agencies in line with the scope and nature of their business, thus addressing potential for regulatory arbitrage. Further, there is a need to formulate specific rules governing the provision of customer information to credit reporting agencies and the resolution of disputes arising from the accuracy and completeness of credit data. An effective information and data sharing scheme should be in place to help lenders make appropriate credit decisions and facilitate access to finance where necessary. The lessons from China’s experience in turn hold key lessons for other jurisdictions as they move from credit information to credit data regulation in their own financial systems.

This paper examines whether China’s switch back and forth from fixed to floating exchange rates in 2005 and 2008 changed the contribution to stock price discovery by foreign and domestic investors. During that time, mainland investors could only trade the RMB-denominated A-shares in the domestic Shanghai and Shenzhen markets, while the dual-listed HKD-denominated H-shares were available only to overseas investors. Using intraday data on overlapping trading hours, we find that the switch from a fixed rate to managed floating in July 2005 increased the H-shares’ contribution to price discovery; while the exchange rate regime reversal in July 2008 allowed the domestic stocks to regain their dominance in information shares. These results imply that, in a market subject to restrictions on capital flows, a flexible exchange rate regime increases the propensity of investors to trade foreign-issued stocks to speculate on the RMB exchange rate, which raises overseas investors’ contribution to price discovery.

The study reveals that the COVID-19 crisis has had a strong but one-off negative impact on the hedge fund industry. It also shows that during the new coronavirus pandemic, the main components of the hedge fund industry achieved only partially their main investment goal, i.e. they as a whole provided a hedge of the investment risk but did not produce higher than the market return in the conditions of a growing capital market. In this situation, due to the relatively stable М&A market, the Event-Driven Risk Arbitrage strategy was undoubtedly most successful, followed by the Emerging Markets, the Global Macro and the Long/Short Equity strategies. The worst performance was reported for the Fixed Income Arbitrage strategy due to the currently overvalued bond markets and to the expectations for higher inflation rates in the countries with developed capital markets.

This paper proposes a novel bond return (price or yield curve) prediction methodology, unifying the classical no arbitrage pricing framework, which is ubiquitous and serves as a fundamental and theoretical building block in mathematical finance, and empirical asset (bond) pricing methodologies, e.g., Bianchi, Büchner, & Tamoni (2021) for treasury bonds and Gu, Kelly, & Xiu (2020) for equities. The methodology can be viewed as a unification of theoretical and empirical asset pricing frameworks. Our method is mathematically and theoretically rigorous, arbitrage-free and meantime enjoys the flexibility offered by the empirical asset pricing framework, i.e., a potentially rich factor structure and accurate function approximations via machine learning regression. Real market back-testing studies show that our predictions are accurate, in the sense that the formulated equally-weighted treasury bond portfolios in China exchange-based markets bear significant positive returns. The average hit rate for yield curve prediction reaches 77.71% across all tenors and the related long-only trading strategy based on the prediction results in an annualized absolute return as high as 12.35% with Calmar ratio achieving 7.31 for equally-weighted portfolios. As a by-product of our prediction framework, spot yield curves can be predicted accurately in an arbitrage-free manner.

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 aim of the study is to investigate the behaviour of the Chinese government bond yield curve before and during the COVID-19 pandemic. Its methodology comprises the techniques of time series analysis, correlation analysis and dimensionality reduction. The main empirical results show that in the pandemic period, the behaviour of the Chinese government bond yield curve differs significantly from that before the outbreak of COVID-19. This is evidenced by the weaker correlations among the analysed yields, the presence of anomalies, heterogeneous behaviour and probable arbitrage opportunities at the long-term end of the studied yield curve, as well as the significant changes in the main factors of its dynamics. The research also reveals that prior to the COVID-19 pandemic, portfolios composed of Chinese government bonds could be well protected against interest rate risk even by using traditional parallel shift immunization techniques. However, after the outbreak of the COVID-19 pandemic the use of such techniques would be relatively effective for portfolios of Chinese government bonds with maturities between 1 and 5 years, while portfolios that include Chinese government bonds with maturities greater than 7 years should be either hedged against all the three factors of the yield curve dynamics or be used only for arbitrage strategies.

This paper dissects the lottery-like anomaly in Chinese A-share stocks by decomposing total stock returns into overnight and intraday returns. Our findings indicate that the negative overnight returns are concentrated among lottery-like stocks, and the lottery-like anomaly is mainly driven by the overnight returns component. Considering the unique Chinese institutional features, our mechanism analysis reveals that the overnight returns induced lottery-like anomaly is more pronounced in stocks with high retail investors' gambling preference and high limits of arbitrage. Overall, our results suggest that investors optimism and trading constraints have a substantial impact on market efficiency in China.